{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"center\"> Logistic Regression (MNIST) </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.\n",
    "<br>\n",
    "It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Used for Confusion Matrix\n",
    "from sklearn import metrics\n",
    "import seaborn as sns\n",
    "\n",
    "# Used for Downloading MNIST\n",
    "from sklearn.datasets import fetch_mldata\n",
    "\n",
    "# Used for Splitting Training and Test Sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downloading MNIST Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Change data_home to wherever to where you want to download your data\n",
    "mnist = fetch_mldata('MNIST original', data_home='~/Desktop/alternativeData')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'COL_NAMES': ['label', 'data'],\n",
       " 'DESCR': 'mldata.org dataset: mnist-original',\n",
       " 'data': array([[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ..., \n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]], dtype=uint8),\n",
       " 'target': array([ 0.,  0.,  0., ...,  9.,  9.,  9.])}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 784)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These are the images\n",
    "mnist.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These are the labels\n",
    "mnist.target.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting Data into Training and Test Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# test_size: what proportion of original data is used for test set\n",
    "train_img, test_img, train_lbl, test_lbl = train_test_split(\n",
    "    mnist.data, mnist.target, test_size=1/7.0, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n"
     ]
    }
   ],
   "source": [
    "print(train_img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000,)\n"
     ]
    }
   ],
   "source": [
    "print(train_lbl.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 784)\n"
     ]
    }
   ],
   "source": [
    "print(test_img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "print(test_lbl.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Showing Training Digits and Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x124c25950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,4))\n",
    "for index, (image, label) in enumerate(zip(train_img[0:5], train_lbl[0:5])):\n",
    "    plt.subplot(1, 5, index + 1)\n",
    "    plt.imshow(np.reshape(image, (28,28)), cmap=plt.cm.gray)\n",
    "    plt.title('Training: %i\\n' % label, fontsize = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0 187 255  98   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0  29 238 253 139\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   5 145 253 253 139   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  66 253 253\n",
      " 253  63   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  19\n",
      "  27   9   0   0   0   0   0 145 253 253 138   3   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0  48 230 253 159   0   0   0   0  26 243\n",
      " 253 253  83   0   0   0   0   0   0   0   0   0   0   0   0   0   0   6\n",
      " 223 253 253 142   0   0   0   0  89 253 253 235  10   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   9 167 253 253 243  31   0   0   0   0\n",
      " 156 253 253 164   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "  39 253 253 220  51   0   0   0   0  18 218 253 253  42   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0  20 206 253 253  78   0   0   0  11\n",
      "  53 128 253 253 253  72  17   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0  94 253 253 253 145  99  99 143 213 253 253 253 253 253 253 219 112\n",
      "   0   0   0   0   0   0   0   0   0   0   0 178 253 253 253 253 253 253\n",
      " 253 253 253 253 253 253 253 253 253 247  54   0   0   0   0   0   0   0\n",
      "   0   0   0  94 253 253 253 253 181 253 179 165 236 253 253 243 165 165\n",
      " 225 209  23   0   0   0   0   0   0   0   0   0   0   6  15  15  15  15\n",
      "   3  15   3   7 209 253 253 167   0   0  10   9   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0  85 253 253 253  45\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   3 204 253 253 246  14   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   6 253 253 253\n",
      " 165   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   6 253 253 253  76   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   6 253\n",
      " 253 244  48   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   4 213 253 128   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0]\n"
     ]
    }
   ],
   "source": [
    "# This is how the computer sees the number 4\n",
    "print(train_img[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Logistic Regression on Entire Dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Logistic Regression Sklearn Documentation](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) <br>\n",
    "One thing I like to mention is the importance of parameter tuning. While it may not have mattered much for the toy digits dataset, it can make a major difference on larger and more complex datasets you have. <b>Please see the parameter: solver</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 1: </b> Import the model you want to use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In sklearn, all machine learning models are implemented as Python classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 2:</b> Make an instance of the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# all parameters not specified are set to their defaults\n",
    "# default solver is incredibly slow thats why we change it\n",
    "logisticRegr = LogisticRegression(solver = 'lbfgs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 3:</b> Training the model on the data, storing the information learned from the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model is learning the relationship between x (digits) and y (labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='lbfgs', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logisticRegr.fit(train_img, train_lbl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 4:</b> Predict the labels of new data (new images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uses the information the model learned during the model training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returns a NumPy Array\n",
    "# Predict for One Observation (image)\n",
    "logisticRegr.predict(test_img[0].reshape(1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  9.,  2.,  2.,  7.,  1.,  8.,  3.,  3.,  7.])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict for Multiple Observations (images) at Once\n",
    "logisticRegr.predict(test_img[0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measuring Model Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "accuracy (fraction of correct predictions): correct predictions / total number of data points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basically, how the model performs on new data (test set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.913\n"
     ]
    }
   ],
   "source": [
    "score = logisticRegr.score(test_img, test_lbl)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A confusion matrix is a table that is often used to describe the performance of a classification model (or \"classifier\") on a set of test data for which the true values are known. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Note: Seaborn needs to be installed for this portion </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# !conda install seaborn -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Make predictions on test data\n",
    "predictions = logisticRegr.predict(test_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm = metrics.confusion_matrix(test_lbl, predictions)\n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x58ctmtv5wNgx/nJqb6qUvx+AG0mpWK3u72RG5ssP/5/6z9PzdW3zCH3CmxPW\naxrHz1xyOxtk7sM19W3VqcDeI3845fuTquWc8jWozBb7bb3cdH2mIX1eaEZY75mG5QPPH2Nxq3x5\nl4GmaVuUUouBYE3Tlhu9/rifdtHy0RqsWzAQk8lExH8+Jbz1w/gX82Heso0MmbSMVbF9MZlMLIrb\nzNkL13JtAzB08nJiR75I0SLeHEw4x7L/7XA7X8snW7F500a6vdQVsDF67PusXbOK5ORkOncJB6Br\nlw74+Pjw8ivdKVGiJM1bPMH27b/xUtfO2Gw2ho0YiZeXl9tZchO3bjctGyvWzXsLkwkiRi8lvHVD\n/P2KMm95PEMmL2dVTCQms1P/5dIG9Bm/a2f1xZJyk1+2Hea7jfuNyWjgGE+a/z1zRv+btJsZJKek\n8fqYpR6VLz/8f+o/T85nNpuYNLgzp85d4bNJvQDYsP0w785e616+dXv0/fGT/vq2Rv+X8LAQPd/y\neIZMiWPVjN76PrxyC2cvXMt1PWaziUlvd+DUuat8NqG7Pd9R3v34W7fygeePsSEK2XmJyaiZ6waz\n+TXoV9AZXLLsiMFizLybfOFXBPwavlnQMVyybJ+Gp48v4LEZPT0f6BklX95ZdsTg9/CAgo7hkmWb\nPtvfU/vQvo+Y/upx7vr9+PV8P4CGVAzK9+eR6a78HAIhhBCioHnk5bQb5IRACCGEyAPPLLDn3V35\ntkMhhBBCGEsqBEIIIUQeyJ8/FkIIIUShIxUCIYQQIi8KV4FAKgRCCCGEkAqBEEIIkSeFrEAgFQIh\nhBBCSIVACCGEyBP5HAIhhBBCFDpSIRBCCCHyQD6HQAghhBCFjlQIhBBCiLwoXAUCqRAIIYQQAkw2\nz5wm6ZGhhBBC3DVM+b2B+CNX8/1Y9VjV4vn+PDJJhUAIIYQQnjuHwK9Bv4KO4JJlRwx+Dd8s6Bgu\nWbZP49y1mwUdw6XS9xTB75GBBR3DJctvkwHwe3hAASfJnWXbFOAu2Ec8fIw9dXxBH2O/x4YWdAyX\nLPHjAM99DVp2xNyR7Vg9s8KeZ1IhEEIIIYTnVgiEEEIIT1a46gNSIRBCCCEEUiEQQggh8qSQTSGQ\nCoEQQgghpEIghBBC5Elh+1sGckIghBBC5IG1cJ0PyC0DIYQQQkiFQAghhMiTwnbLQCoEQgghhJAK\ngRBCCJEX8rZDIYQQQhQ6UiEQQggh8kDmEAghhBCi0LkrKwQmk4lpw8MJrl6G1LR0IscsIeHURcfy\nNs3rMDwYsvDkAAAgAElEQVTiGdIzrCxcEc/85ZtctqlRuTQzR7yIyQRHTl4gcsxSMjKs7ucb2oXg\n6g/p2xr7GQmnnfI1q83wXq1Jz8hg4cotzF8e77JN/RplmTHsBVJvprNbO0PUxGXY3LxxZbVamfLh\nWI4cPkTRokUY9M4YypYr71j+w7er+XzJQsxmM22e7cDznbuSlpbGuDEj+OPsaYr5+zNg0AjKlq/A\n6VMnGTfmHcBEpSpVGTB4BGaz++eZJpOJaUM6EVztIVJvphP57hc5+rAWw3s+TXq6lYWrtjJ/xWbH\nskdql+fdN9oR1icWgEXvvcwD9wYCUOHBkmzde4Ju7yx2P9/Qzln5xn5+6xj3fFp/Da7ccmu+/s8S\n1nsmADUqPcDMd17AZDLpr8F3PzfmNejp+4hB43t/iQBmvvMCJQL98PIy0+M/Szl25pL7+Qwa30zh\nYSFEhjejxWvT3MrmyDfoOYKrPkjqzQwiP/iahNNZz7lNaE2Gd2+p51u9jfkrf8NsNhE7rBPVy9+H\nzQZvjF/O/oQ/Ca72IJMHtifDaiM1LZ2eY77g/JUbxmQ06DUYXL0Mk4d0ycoYvYjzlxPdzugu+RwC\nD9D+iWB8i3rT4pVJRE+PY9zAjo5l3t5mxkd1ol1kDK16TKVHp6aUKhnoss2Yfs8yMmYlLbvrf2O+\nbfM67udrURdfH29adJ9K9IxVjBvwfI58HWjXN5ZWvWbQo0MTPZ+LNjHvhDNo0jKe6jmdazcshLdu\n6Ha+X3/5kbS0NGbNW0JE3wHETpuQbXnstIlMjpnLzLmf8sXShSRev8bqFV/hV6wYs+Yt5c23hzN1\nwnsAzJw6nh593iBmziKw2fj1l5/czgfQvkUdvT96TCc6Zg3j3mrvWObtZWb8gOdp1+8jWvWeSY8O\nj1KqZAAAA19+gtgR4fgWLeJ4fLd3FhPWJ5bwQfO5esPC4MkrjMlX1JsWr00jesZqxg3IkW/gc7Tr\nN5tWETH06PBYVr5uLYmNDse3aNa5+Ji+bRk5cw0te0wHoG2z2u7n8/h9xLjxfa9/Oz7/djutes9k\n1KxvUBVLGZPPoPEFqKfK8MpzjTGZ3I6m52teC9+iRWgRMYvo2G8Y90bb7PnebEu7t+bR6vWP6fFc\nI0qVCKBtaE0AWvaezaiPvmdU7zAAJg54loGTVxLW92PiftlL1MuPG5PRwNfgxMGdGfjhl4T1mkbc\nTzuJ6t7KkIwiuzt2QqCU8jFqXU0aVOGHTQcA2LrnOA1rZV3d1qhUmqOnLnA10cLN9Aw27ThKaEhV\nl226vj2Xjb8fpYi3Fw/cG8S1Gynu56tfOWtbe0/QsFa5rHwVS3P01MWsfDsTCA2p4rJNmVLF2bz7\nOADxu47RpH5lt/Pt3rmDRo81BaB23XpoB/ZlW16lanWSbiSSlpqqVyNMJo4fO0rjx0IBKF+hEieO\nJwBw6OB+6oc8AkDjJs3Y/ttmjNCkXiV+2HQQsPdHTac+rPQAR0879+ExQhtUASDh9CW6Dp6f6zqj\nI1oz6/NfOXfJ/SuLJvUr80P8bfI5j/Eu53wX6Tooe76ug+ezcUeC/TUYyLUbFvfzefo+YuD4PhZc\niTKlirNmZh+6tg5h/faj7uczcHxL3lOM0a+3ZdAk909EHfnqVeSHzZqeb98pGtYsk5WvYimOnr6U\nlW/3CUIbVGLV+v30HbcMgPIPFne8zrpF/5fdh/8A9JOJlLR0YzIa+BrsNnQ+uw+dsWf0IiX1piEZ\n3WW7A//dSYafECilnlVKnVBKHVFKhTst+saobQT6+2b7pZmRYcXLS38qQf6+XHdalpicSlCgr8s2\nVquN8g+W4Pev3+HeEgHssb/o3MoX4Jvtl2aG1ZaVLyBnvhSCAnxdtjl+5hKhIfovmzbN6+DvV9Tt\nfMlJN/APCHR8bzabSU/P+iVQqUo1er3yAq90fY7HQh8nMDCIqtVrEP/rL9hsNvbt2cXFC+fJyMjA\nZrNhsl/2FCvmT9INY8p4gf6+XEty7o+/GOMAXwBWrNvNzfSMW9Z3f4kAWjSqxuLVW43L5/x6ch5j\nf1+uO41lYlJKVr6fbs1ntdooX7oEv38xhHuLB7Dn8Fnj83naPmLg+FZ4qCRXEpNp23c2p/68StQr\nLY3JZ8D4ms0mZkd3ZciUFSQmu38ilT2fU/9lOOfzyZ4vOZUgf1/746zMie7C5IHt+ey7nQCOE+RH\n65anT+cmzPjsVwMzGvMaPHfxup6xXiX6hDdnxpJ1hmQU2eVHheAdoD7QGOitlHrF/nODimX6DhhY\nLKvgYDabHPc0ryelEGB/8QMEFvPhWqLltm1O/nGFus+NYe5XG/gwKqusled8N1II9Hfalskp340U\nAoo55/PV87loEzF6KYO6t2LtrL5cuJzIpatJbucr5h9AclLWemw2G97eeonz6GGNzRvX89mK7/g8\n7nuuXLnMuv99R5tnO+DvH8AbEd3Y8POPVK9RCy8vr2zzBZKTkwgIDHI7H+QyxqYcY1zs1jG+nQ5P\nBvP5t79jNeimn54vK8Mt+ZzGMucv79ycPHeFuh3fZ+7XG/nQ6RaTe/k8eB8xcHwvXUtizXq9yrV2\n/T5CnK7m3cvn/viG1CxHlXL3M31YFxa/340alUozYaBB4+vvanxTCXDq28BiPtkOsr3GfknwCxOJ\nHdqRYr76rZfOTwYzfXAHOkQt4KIBv2McGQ18DXZ+OoTpw7vSof8sLhowx8EINlv+f91J+XFCkKZp\n2hVN0y4BzwH9lFJPgHG1j/idCYSF6vdZG9WtyN4jWVdUB4+do2r5+ykRVIwi3l40DanKll3HXLb5\ncmpvqpS/H4AbSamGHDDidx0jrGktfVt1KmTPdzxnvips2X3cZZtnQmvRfcQi2kTO5N57/Plxi+Z2\nvrr1GrBl0wYA9u3ZRaUq1RzL/AMCKerjg4+PL15eXpQoUZLExOsc3L+XkEcaEzNnMS2efJqHypQF\noGr1GuzYrl91b9m0geD6IW7nA4jfdZywpvo9z0Z1KrD36B+OZQeP/UnVcvdl9WGDymzZc+K262vZ\nqDrf20uRxuQ7lj3fkZz57s+ez37bJzdfTu5BlXL3AXAjORWr1b0Je3A37CPGjW/8zmOENdHXFRpS\nmQMJ5wzIZ8z4btt3kobhHxLWeyYvD1/EwWPnGGTAHJb43ScIe6yGnq92OfYezXrOB4+ft/efn56v\nfkW27D3Ji60b8Ha3FgAkp9zEarNhtdnoGlafPp0fI6zvxxw/e9ntbI6MBr4Gu7Z5hD7hzQnrNY3j\nbk4YFa7lx7sMjiulJgPRmqYlKqU6At8BxY3aQNxPu2j5aA3WLRiIyWQi4j+fEt76YfyL+TBv2UaG\nTFrGqti+mEwmFsVt5uyFa7m2AZg0/3vmjP43aTczSE5J4/UxS93Pt243LRsr1s17C5MJIkYvJbx1\nQ/z9ijJveTxDJi9nVUwkJrNTvlzagD6re+2svlhSbvLLtsN8t3G/2/matXiSbVs28XqPl7DZYOjI\nsfzw7RoslmTad+hC+w5d6NfrZYoUKcJDZcrxTLvnSUq6wZh3Ylg8fw4BgYEMGTEGgL5vDmLC+6P4\n+OY0KlSqxOMtn3Y7H0Dcz3to2bg66z55AxMmIsZ8RnhYCP7FijJv+WaGTI1j1YwIfYxXbeXshWu3\nXV+1CqXcnnmeLd+6Pfp4fdJffz2N/q89n48+xlPiWDWjtz7GK7fcNt+kBT8yZ9S/SLuZTnLKTV4f\n+7n7+Tx9HzFwfIdOXUnsiBeI6NyEazdSeHXEp+7nM3B880PcL/to2agq6z6OxAREvPcV4U/Xw9/P\nh3lxWxkyfQ2rprym51u9jbMXrhP3814+HtGFH2J7U8TbzKCpq0m7mcGkge05de4qn33wMgAbdiTw\n7tz/uZ/RoNeg2Wxi0uDOnDp3hc8m9dIzbj/Mu7PXup3RXYXtkwpN7r6FLSellDfwb+ALTdOS7T97\nABimadpbf3M1Nr8G/QzNZSTLjhj8Gr5Z0DFcsmyfxrlrnjHpJjel7ymC3yMDCzqGS5bfJgPg9/CA\nAk6SO8s2fba/x+8jHj7Gnjq+oI+x32NDCzqGS5b4cYDnvgYtO2LAwNvUrny770K+nxK0rn1/vj+P\nTIZXCDRNSwcW5PjZn8DfPRkQQgghPJ5VPqlQCCGEEIXNXflJhUIIIURBK2xzCKRCIIQQQgipEAgh\nhBB5Udj+2qGcEAghhBB5ILcMhBBCCFHoSIVACCGEyAN526EQQgghCh2pEAghhBB5IHMIhBBCCFHo\nSIVACCGEyINCViCQCoEQQgghpEIghBBC5InRfy24oEmFQAghhBBSIRBCCCHywlrQAQxm8tCSh0eG\nEkIIcdcw5fcGvtr1R74fqzrXezDfn0cmj60Q+DXoV9ARXLLsiMEvpH9Bx3DJ8vt0/B4bWtAxXLLE\nj+Pc9ZsFHcOl0kFFAM99DVp2xADg13hQASdxzbJlgsf2H9wl+7CHjy+A38MDCjhJ7izbptyR7Xjo\nBXWeyRwCIYQQQnhuhUAIIYTwZIWrPiAVAiGEEEIgFQIhhBAiT2QOgRBCCCEKHakQCCGEEHlQ2D6H\nQCoEQgghhJAKgRBCCJEXhW0OgZwQCCGEEHlQyM4H5JaBEEIIIaRCIIQQQuRJISsQSIVACCGEEFIh\nEEIIIfLEWsgmEUiFQAghhBB3Z4XAZDIxbXg4wdXLkJqWTuSYJSScuuhY3qZ5HYZHPEN6hpWFK+KZ\nv3zTX7YZH9WRQyfOM/erX43JN6xL1rbG/vfWfL3C9Hxxm5m/PN5lm/tLBDAz+kVKBPnhZTbTY+Sn\nHDt98TZb/5v5Bj1HcNUHSb2ZQeQHX5Nw+lJWvtCaDO/eUs+3ehvzV/6G2Wwidlgnqpe/D5sN3hi/\nnP0JfzrajH+zHYdOXmDu8i1uZctktVqZ8uFYjhw+RNEiRRg0Ygxly5V3LP/hm9V8vmQhZrOZNu07\n8HznrqSlpTFuzAj+OHOaYv7+DBg8grLlK3BIO8CwAX0d7Z/rFE7Lp59xK5+Rr8HK5e5jzuiXsdls\n7Dv6B2998IXbb2cymUxMG9yB4GoP6dt6/8tbx7hHKz3fqq3Mj9uKt5eZj6JfoMKDJfAp4s24+T+y\nZsN+R5vwp+sT+UIoLXrGuJXNkc+g/guuXobJQ7qQYbWRmpZOz+hFnL+c6H4+g/bhRR+8wgP3BgFQ\n4aGSbN1znG7DFrqfz6DxrVGpFDOHdcaEiSOnLhL5/pdkZLj/kTsmk4lpQzvrGW+mEzn2cxKcfne1\naVab4T2f1jOu3ML8FZsdyx6pXZ53+z9LWO+ZAARXf4jJgzqRYbXqY/yfJZy/fMPtjO4qXPWBu7RC\n0P6JYHyLetPilUlET49j3MCOjmXe3mbGR3WiXWQMrXpMpUenppQqGeiyzX0lAlgRE0nbx+samK8u\nvkWL0OLVKUTPWMW4AR1y5OtAu9djadVzOj06NrHny73Ne28+x+ffbKNVz+mMil2DqljK/XzNa+nb\niphFdOw3jHujbVY+LzPj32xLu7fm0er1j+nxXCNKlQigbWhNAFr2ns2oj75nVO8wAO4r7s+Kyd0d\ny43y688/kpaaxqx5S4joN4DYqROyLY+dNpHJM+cy85NP+WLJQhKvX2P1iq/w8yvGrPlLefPt4Uyd\n8B4Ahw7s44V/dWPaRwuY9tECt08GwNjX4IdRnRg1czVP9ZiKyWTi2RbuvxbbP15bH+OeMUTHrmXc\nm89m5fMyM/6t9rTrP4dWfWbR4/lHKVUygBefCeHytWSe6j2L9m/NZcrbzzva1Kv+EK+0b4TJ5HY0\nPZ+B/TdxcGcGfvglYb2mEffTTqK6tzIgn3H7cLdhCwmLmEF41FyuJloYPGm5+/kMHN8xkc8wMvYb\nWkboB9+2obXczgfQvkUdfbxem0b0jNWMG9A+e8aBz9Gu32xaRcTQo8NjlCoZAMDAbi2JjQ7Ht2jW\n9erEqA4MnPA1Yb1nErduN1GvPGlIRpHdHTkhUEr5KaV8jFpfkwZV+GHTAQC27jlOw1pZV441KpXm\n6KkLXE20cDM9g007jhIaUtVlG38/H96bvZala34zKh5N6ufcVrkc+S5m5duZQGhIFZdtHqtfiTKl\nirNmVl+6PvMw67cdcT9fvYr8sFnTt7XvFA1rlsnKV7EUR09fysq3+wShDSqxav1++o5bBkD5B4tz\n7YYFAH+/orw3938s/XaH27mc7d61g0ZNmgJQu249tAP7si2vUq06STcSSUtN1a+mTSaOJxylcZNQ\nPWPFSpw4lgCAdnA/8RvX80bEK3w4NprkpCS38xn5GgypWY4N2w8D8P3GfTzRuIb7+epV4ofNB/Vt\n7T1JwxplnfI9kH2Mdx0jtH5llv24m9EffQeACRPp9qvEkkHFGB35DIOmrHQ7lyOfgf3Xbeh8dh86\nA4C3lxcpqTfdz2fgPpwpuk8bZn22nnMXr7ufz8Dx7Tp0ERt3HqOItxcP3Bvo2Lfdzli/Mj/EZ2Y8\nQcOazn34QPY+3HWM0AZVAEg4fZGug+ZnW1e34YvZfegsoJ9MGDHGRrDZbPn+dSflywmBUqqWUmqF\nUmq+Uuop4ACwXynVzoj1B/r7ZnvRZmRY8fLSn0qQvy/XnZYlJqcSFOjrss2Js5f4be8JI2LlLV9S\nKkEBfi7bVHjwXq4kJtM2cianzl0h6tWnDMqX4rQtm1M+H647LUtMTiXI39eRaU50FyYPbM9n3+0E\n4MQfV/ht/ym3M+WUnHQDf/9Ax/dms5n09HTH95UqV6NXtxd4Jfw5Hgt9nMDAIKpWr0H8r7/opfc9\nu7h44TwZGRnUrFWXyP5RzPh4IQ+VKcuCObFu5zPyNWhyuuxOTErlngBfA/L5ZB9jqzXHGOfIF+BL\nkiWNG8mpBBTzYem4lxk9+1vMZhOzR3RhyLRVJCanup0rK59x/Zd5gH20XiX6hDdnxpJ1dzbfX+zD\nAPeXCKBFo+osXmXMLTWjxhfAarVRvnRxfv8sinuLF2PP4T8MypijP6y2HH3o9HsmKYUg++t+xU+7\nuZmekW1d5y7Zxzi4In1eaMaMpb8YklFkl18VgtnAFOBn4CugEdAAGGbEyhOTUggsllVwMJtNjnte\n15NSCPDP+oUaWMyHa4mW27YxWmJSCoFOGcxmc/Z8TjkC/Z3y5dLm0rUk1vyyB4C16/cSkuOqI+/5\nXPVfavZ8xXyy7dS9xn5J8AsTiR3akWK+RdzO4kox/wCSk7Ou5G02G97eegnx6GGNzRvX81ncd3y+\n8nuuXLnMuv99R5v2HfD3D+CNXt3Y8POPVK9RCy8vL5o98SSqZm0AmrV4isPaQbfzGfkatFqzXoeZ\nrwf386XeJl8qAf65j3HZUvfwbWxvln7zO59/v5OQGmWpUu4+pg/uyOJ3X6JGpQeY4FT6zXs+Y/fh\nzk+HMH14Vzr0n8XFK+7fWzZyHwbo8FR9Pv92O1arMVd8Ro1vppPnrlK383jmLtvMh29l3X5wL2MK\ngcWc+sOUc4yd+zD7RUpuOreqz/RhXejw1hwuXnW/ymcE6x34uh2llFkpNVspFa+U+lkpVTXH8keU\nUhuUUr8qpb5SSt32aiO/TgjMmqb9omnaQmCFpmnnNU27DqT/VcO/I35nAmGh+i/4RnUrsvfIWcey\ng8fOUbX8/ZQIKkYRby+ahlRly65jt21jtPidCYQ1rfX38+0+5rKNc+7QkCocSDjnfr7dJwh7TC9L\nN6pdjr1Hs9Z58Ph5qpa7jxJBfnq++hXZsvckL7ZuwNvdWgCQnHITq82Wr2+5qVuvAVs2bgBg355d\nVKpSzbHMPyCQoj4++Pj44uXlRYkSJUm8fp2D+/cS8khjYuYupsWTT/NQGb2MOuiN3hzYp59Ubf9t\nM9Vrun+P1MjX4M6Dp2nWUH9+TzetzcYdR93Pt/s4YU30eR2N6pRn7xGnMT72Z/YxblCZLXtOUKpk\nAKum92JEzFoWrdJvoW3bf4qGL04i7PXZvDxiCQeP/WnIrQMj+69rm0foE96csF7TOH7m0q0by2s+\ng/ZhgJaNFd9v3I9RjBpfgC8nvEqVcvcBcCM51bD9On7XMcKaZmaswN4jWZUHPaNTHzaozJbdx12u\nq+szDenzQjPCes80bIwLiecBX03THgOGApMyFyilTMAcoLumaaHAt0CF260sv95loCml5gIRmqa9\nag83FHD/aAbE/bSLlo/WYN2CgZhMJiL+8ynhrR/Gv5gP85ZtZMikZayK7YvJZGJR3GbOXriWa5v8\nErduNy0fVaybPwCTCSJGLSG8dUN7vk0MmbyCVTMjMZnNWflyaQMwdMpyYqNfJKJzKNduWHh1uHuz\nkwHiftlHy0ZVWfdxJCYg4r2vCH+6Hv5+PsyL28qQ6WtYNeU1TGYTi1Zv4+yF68T9vJePR3Thh9je\nFPE2M2jqalJSDTm/y1WzFk+ybcsmXn/tJWzA0JFj+eHbNViSk2nfsQvtO3ahX8+XKVKkCA+VLccz\nzz5PUtINxgyPYfH8OQQEBDIkegwAA4dGM23C+3h7e1Py3vt4e/got/MZ+RocOnk5sSNfpGgRbw4m\nnGPZ/9yfjxH3815aNqrGujl6hoixnxP+dH0934otDJm6ilXTeuljvOo3zl64zsSB7SkeVIxhrz3F\nsNf0W1PPDZibL+NsVP+ZzSYmDe7MqXNX+GxSLwA2bD/Mu7PXupfPwH0YoFqFUhw7bdyBzMjxnbRo\nHXOiw0lLTyc55Savv/elMRnX7aFlY8W6T/rrGUf/l/CwED3j8niGTIlj1YzeesaVWzh74Vqu6zGb\nTUx6uwOnzl3lswndAdiw/SjvfvytITnd4QEfQ5B5oEfTtM1KqYedllUHLgEDlFJ1gDWapmm3W5kp\nPyYtKKXMwLOapsU5/ezfwDJN05L/xipsfg36GZ7LKJYdMfiF9C/oGC5Zfp+O32NDCzqGS5b4cZy7\n7hmTgnJTOki/FeKpr0HLDv1tf36NBxVwEtcsWyZ4bP/BXbIPe/j4Avg9PKCAk+TOsm0KgEHviXHt\nk60n8/2UoEej8i6fh/3C+2tN076xf38SqKxpWrpSqinwPyAEOAKsBj7UNO0nV+vLlwqBpmlWIC7H\nz/LvklwIIYS4wzzgkwqvA4FO35s1Tcss6V0CjmiadgBAKfUt8DDg8oTgrvwcAiGEEEKwEWgDoJR6\nFNjjtCwBCHCaaNgMyP7+7Rzuyk8qFEIIIQpawRcIWA60UkptQr9F0l0p9S8gQNO0j5VSPYCl9gmG\nmzRNW3O7lckJgRBCCHEXst+e75Pjxwedlv+E/rb/v0VOCIQQQog88IA5BIaSOQRCCCGEkAqBEEII\nkRcGffCkx5ATAiGEECIPCtkdA7llIIQQQgipEAghhBB5YqVwlQikQiCEEEIIqRAIIYQQeSFzCIQQ\nQghR6EiFQAghhMiDwva2Q6kQCCGEEEIqBEIIIUReFLaPLjbZPPMJeWQoIYQQdw1Tfm9g8vqEfD9W\nDWxeOd+fRyaPrRD4hfQv6AguWX6fjl+DfgUdwyXLjhjpPzdYdsQAcCkpvYCT5O5ef3239fQ+9Gv4\nZkHHcMmyfZrn7yMe3n8AfqHRBZwkd5Zfx96R7Xjm9XTeyRwCIYQQQnhuhUAIIYTwZPIuAyGEEEIU\nOlIhEEIIIfLAQyfl55lUCIQQQgghFQIhhBAiL2QOgRBCCCEKHakQCCGEEHkgFQIhhBBCFDpSIRBC\nCCHywFbIPmVfKgRCCCGEcF0hUEqNvF1DTdPGGB9HCCGEuDsUtjkEt7tlcMf+wpIQQghxtylkn0vk\n+oRA07TRmf+vlPIHqgB7AT9N05LuQDYhhBBC3CF/OalQKdUS+BjwApoAu5VSL2ma9n1+h3PFZDIx\nbVgXgquXITUtncix/yXh1EXH8jbN6zC8VxjpGVYWxm1m/vJ4l23uLxHAzOgXKRHkh5fZTI+Rn3Ls\n9MXbbP1v5hsenrWtMUtuzRfxjJ5vRTzzl2/6yzbjozpy6MR55n71q1vZHPkM6r96qizLpkVw5OQF\nAOZ89Stffb/DmIwG9WHlcvcxZ/TL2Gw29h39g7c++MLtjxy1Wq1M/GAshw9pFC1alGHRoylbvoJj\n+TerV7J00XwCAgJo0/55nn2+k2PZvj27iZ0+mZlzFgBwSDvAhPfG4OXtRbnyFRk2cgxms3vTe4zs\nv+DqZZg8pAsZVhupaen0jF7E+cuJ7ucb2oXg6g/ZX0+fkeC037VpVpvhvVqTnpHBwpVbmL883rHs\nkToVePeNZwnrHZNtneMHdtD3ka83upXNkc+gfSRTeOuGRHZtTotXpxiTz4P7z5Exqh3BVUuTejOD\nyHErSDhzOStjU8XwV1vofbjmd+av2u5Ydn9xfzZ9EknbAQs4dPIiwVVLM3lA26zX4Ltfc/5KwV+X\nWgtZieDv/Nb5AAgFrmqa9gfwODAhX1P9hfZP1MW3aBFavDqF6BmrGDegg2OZt7eZ8VEdaPd6LK16\nTqdHxyaUKhnoss17bz7H599so1XP6YyKXYOqWMqAfMH4FvWmxSuTiJ4ex7iBHXPk60S7yBha9ZhK\nj05N7flyb3NfiQBWxETS9vG6bufKymdc/zWoWY7pn64jLGIGYREzDDkZ0DMa14cfRnVi1MzVPNVj\nKiaTiWdbuN+X69f9SFpaKnMWLiXyjQFMn5K1S1y9coU5s2Ywc858Zs5dyHdrV/PH2TMAfLrgEz4Y\nO5K01FTH4+d9PIvuEZHMnvcpN2+msWnDL27nM7L/Jg7uzMAPvySs1zTiftpJVPdW7udrURdfH29a\ndJ9qfz09nyNfB9r1jaVVrxn06KC/BgEGdmtJbHRXfH2KOB5/X3F/VkzvTdvH67idy5HPwH0EoJ4q\nyyvPP4rJZMydWE/vP4D2zWrqr6c+c4ie/T3j+rXOyuhlZvwbz9Bu4EJa9ZtHj/YPU6qEv2NZzOD2\nWNJuOh4/8c02DJyyhrA35hG3fj9RLzUzNKvQ/Z0TArOmaecyv9E0bf8/2YBSyv0jbA5N6lfhh00H\nAOW01DMAACAASURBVNi65zgNa5VzLKtRqTRHT13kaqKFm+kZbNqZQGhIFZdtHqtfiTKlirNmVl+6\nPvMw67cdcT9fg5zbKp8j34WsfDuOEhpS1WUbfz8f3pu9lqVrfnM7lyOfgf3XoGY5WjerzQ9z+zNr\n5IsEFPMxJqOBfRhSsxwbth8G4PuN+3iicQ238+3a+TuNm4QCUCe4Hgf373MsO3vmFFWrK4LuKY7Z\nbKZm7Trs3bMLgDLlyvHBxGnZ1lVd1eD6tWvYbDaSk5Lw9nb/3cBG9l+3ofPZfUg/ofH28iIl9Sbu\nalK/cta29p7I/hqsmPtrECDh9CW6vj0v27r8i/nw3sffeuw+UvKeYozu145BE5cZmM+z+w+gSXB5\nftii/z7duu80DWuUccp4P0fPXOZqYoqecfdJQutXBGBcv9bMWfEbf1zMqkJ1G/UFu4/ohyFvLzMp\naemGZs0rqy3/v+6kv3NCcFop1Q6wKaWKK6XeAU66erBSqrrzF7DS6f8NEejvy7UbFsf3GRlWvLz0\npxLk78t1p2WJSakEBfi5bFPhwXu5kphM28iZnDp3hahXn7qz+ZJTCQr0ddnmxNlL/Lb3hNuZ8pzv\nL/pv274TDJ8aR6ue0zl25hLvRGRdBdyxjH/Rh85XZYlJqdwT4Ot2vuSkJAICAh3fe3mZSU/Xf0mV\nLV+BY0ePcPnSRVIsFrZv3UKKRc/1xJNP33LAL1u+AlMmvM+LnZ7l8uVLNHi4kdv5jOy/cxevA/Bo\nvUr0CW/OjCXr3M8X4Mu1GylZ27LasvIF5MyXQpB9zFb8tIub6RnZ1nXi7GWP3UeKFvFm9sh/MWTy\nchKT/o+9O4+Lqt7/OP6aYd/c85aKux5XFCkzd70Spmauoff+Kg3FcN8XzNLSNFOURdRwSStvdQ1F\n0kpLbpnivuB6THHNNJdEhAEF5vfH4DCoY8QZZKLP8/HgkXTme857PmfhzOecmcnrCmnOZ+f1A/Dy\ncCElzTKjZQ1duGWRPzU9k1IervzfC75cvZnGd7vzvzC7fP02AC0aefNGrxZEfrHD5nlFwT6YaAgQ\nDngDycD3QPAjHv8dkA5cwvROBQVYChiBjlrC3pOaloGXR95BXa/Xk52dA8CttIx8r1K9PFxISTVY\nHXM9JY2NPxwGYNOPR5g+rKtt8llk0Ot1+fNZ5PByt8hnZYyt2bJ+G7YmmQ+CG7YmETYp71q55ow2\nqmFOTl4d7z0frdw9PEhPy7uGmZNjNP+hL1WqNKPGTSJ0wmhKlS5D3Xr1KV2mrNV5LfxgDouXf0zN\nWrX58vM1RIbNZfyUaZry2Xob7PN8MyYGBdBz5GKu/X5bUzaA1NsZeHlYLEtnke92Bp7ulvlcbbLO\n/lQ+G+0jPnUrUavqE0RMeRlXFyfq1XiSD8b30twtsPf6gelEKd/2ZJkxLTN/Dd1dSLmdwdA+LTAa\njXR8uhY+tZ9k+Zu96TP5U67cuE2fjo2Y+Go7ek78mGs30x/783mYEnYLwR93CFRV/U1V1f6Y3mVQ\nWVXVvrn3EljzNHAMmK2qagfgoKqqHVRVtcnJAEDiwWQCWjUAoHnj6hw5dck87cSZy9Su+gRlS7nj\n5OhAq2a12ZV0xuqYxIPJBLRuCEDrZrU4nnwZrSznWaB8h848coyt2bJ+8YtCeLqhqbXcoXldDhy/\nYLuMNqrhwRMXaeNXB4DnWzVk+4HTmvP5NPUlcfuPABxJOkSt2nXM07KyslBPHGfx8o+Z+X4Y586e\nwaeJr9V5lSpdGg8P0/XTCk9UJDX1luZ8tqxfvy7P8EZgWwIGh3P2l+uaswEkHjqTtz01qpY/39n7\n89ViV9JZmyy3wPlstI/sPXoev76zCQiO5JXJH3HizGWbXDqw9/oBJB4+T0AL037RvGEVjiRfsch4\nldpVylPWy82UsWk1dh05j//w5Tw/YgUBI1aQdOoyQTO/5MqN2/R7vglv9H6WgBErOHvp98f+XP4u\nCvIug8bAKqBq7u8ngNdUVX3oUVVV1d8URXkZmKcoyjO2DHtPXEISHVsoJKwcg04HwdM/JbCzHx7u\nLqyI3cGksPXELwpBp9ezOm4nl66mPHQMwOQF64ie1p/gPq1JuW1gQOgq7fm2HqJji3okfDQWnU5H\n8NufENj56dx825k0P5b46GHodLq8fA8ZU1RsWb+Rs78gbGIf7mZlc+X6LYbN/Nw2GW1Yw8lh64h+\nqz/OTo6cSL5M7Hfab3xs16ETe3YmEjzg3xiNRqZOn8nmr78iPT2dHr1fBmDAv/rg7OxC/1deo0xZ\n6x2CKdNm8NaU8Tg4OOLk5MTkaTOsPragbFU/vV7H/Il9uHD5dz6bPxiAbft+ZuaSTdryJSTR8VmF\nhBWjTdvTjDWmbdDNmRXrEpkUto74qBB0+rx8j5Mt95Eiy2fH9QOI+/E4HZ+pRcLiwaaM760j0N/H\nlHHDXiZFfU182KumjBv3c+naw9+5otfrmD+6CxeupPDZe/0B2HbgLDNXbH2cT+ehStq7DHR/9PYr\nRVG2AzNVVf069/eewGhVVdv90cwVRRkADCzIY+9jdGs28k8OeXwM+yNw8x1e3DGsMhyIQupXeIYD\nprdjXU+zjxuX7lfew3Qeb+81dPMbVdwxrDLsC7f/fcTO6wfg1lrbpa2iYvjpXXgMH6439euTRX5G\nMOuFuo/tQwILclOh272TAQBVVdcBpQoyc1VVPyrEyYAQQghh94zGov95nB71XQb33qd0SFGUycBy\nIAv4N7DtMWQTQgghxGPyqHsIfsD0zgAd0B7Tuw3uMQL2228TQgghiljRvA+s+DzquwxqPM4gQggh\nhCg+BXmXgQIMBTwxdQscgBqqqrYt4mxCCCGE3Spp7zIoyE2FnwM3AV/gIFAR07ceCiGEEKKEKOh3\nGbwNfAPsB3oAzxZpKiGEEMLOlbR3GRTkhCBdURQX4CTgp6pqJqD9w+CFEEIIYTcK8l0GnwDxmN5u\nmKgoSmfglyJNJYQQQti5x/1thEWtIN9lEAX0VlX1Kqa3H36I6bKBEEIIIUqIR30w0Vv3/W75a2Pg\nnSLKJIQQQti9P/ro/7+aR10yeGyfnyyEEEL81ZS0SwaP+mAi7V+5JoQQQoi/hILcVCiEEEKI+5S0\nDkFB3nYohBBCiBJOOgRCCCFEIZS0mwp11p6Qoig5mL7VEB68wdCoqqpDEeYqWVUWQgjxuBX5jfEj\n1h0v8r9VkT3rP7Yb/B91U2GxXk5we3pMcS7+kQx7F+DWzH6//dmwPwI3v1HFHcMqw75w3FpNLe4Y\nVhm2zwLsdxs07F0AQPLVjGJOYl3NJ1xxaz6+uGNYZdg9z/734ZahxR3DKsOO9wD730eK2t/m64/v\nURSlIqZPKbz/2w5fLeJsQgghhHhMCnIPQSxwGmgBrAeeBw4VZSghhBDC3pW0ewgKclmggqqqr2H6\nPoNYTB9f3LAoQwkhhBDi8SrICcHvuf9VgSaqqqYATkUXSQghhLB/Je3rjwtyyWCroij/BcYDmxVF\naQbY791MQgghhPjTCvJth1OByaqqngP6Y+oU9CzqYEIIIYQ9yzEai/zncSrIuwxezf1vq9z/dR3w\nB1YXYS4hhBBCPEYFuWTQweLfTkAb4EfkhEAIIcTfWAl7k8EfnxCoqjrQ8ndFUcoBnxdZIiGEEEI8\ndoX5LoPbQHUb5xBCCCH+Ukra5xAU5B6CBPJ/p0FNYFNRhhJCCCHE41WQDsF0i38bgWuqqh4rmjhC\nCCHEX0MJaxAU6ISgj6qqIyz/h6Ioq3I/vVAIIYQQJYDVEwJFUZZhujzwtKIolh9V7ASULupgQggh\nhD173J8TUNQe1SGYienmwXBMlw3ufSdzFnC8SFP9AZ1OR/jkPvjUqUTm3SxC3v2c5IvXzNO7tGlI\n6KDnycrOYdWGXaxcv9M87ZmGVZk58kUChiwCoF6Nf7Bo6svodDpOnb9KyMzPyc7W9qWWOp2O8Cl9\n8albmcw7WYS8+x+SL1jka9uI0MEBpnxxO1m5LvEPxwR29iOkX1vaD9D+tZ6m+vXFp26l3GV99mD9\nBncmKzvbVL91ieZpzzSqxswRLxIwJAqAmlUqEDPj3xiNRo6e/pXRc9ba5EYbnU5H+Pju+NR+0pRx\nzjqSf7mRl7FVPUIHdjDV8Kt9rIzfa572RBkPdqwYStfRKzl5/ho1K5cjZmpvjMDR5CuMnh+vOaMt\nt0GfupUIm9Cb7JwcMu9kMejtT/ntxm1N+XJyclg0fxbJp07i5OTM6MlvU6lKVfP0rZs3EvvZavR6\nB57v2oNuPV82T7v5+3VGBPXnvQVL8a5Wg1MnjzN94ggqVakGQNeefWn3z86a8ul0OsIn9cKnzlNk\n3skmZNYXJF+8bp7epXUDQgf5526De1gZtwtHBz1LpwVSrVJZXJwcmbPiOzZuy7t6OXdMd06eu8qy\n2MSHLfLP57PRPtxEqUJseDCnzl8FIGbtT6zdfEB7vvHdc+uXRcjs2Af3j9c75u4fe1m5YS96vY7o\nyT2pW/UJjEYjIz6I41jyFVa/049/lPMEoNpTZdl99AKvvvWZpnzmjHZ8nLaFknU68IhPKlRV9ayq\nqv8DWgONVVX9ATgFBFDMH13cvX0jXJ0daf96ONMiv2LOmO7maY4OeuaOfYluw5fgHxxFUM/nqJi7\nsY99tSPR0wJxdc47D3pnWFfeWrSRjkERAHRto/17m7p3aIyrsxPtByxgWmQ8c8bkfbCjo6OeueN6\n0m1oNP6DIgjq1ZKK5bweOaaJUoXXerRAp9M9bHF/Pl/7xri6ONJ+4MLcZfV4MN+waPwHRxLU05QP\n7tWvH64ueV9l8f7YHkyP3kinQRHo0PFi+8a2ydi2vmkdD1nKtCWbmTOiS15GBz1zR3ah25iV+A9b\nRtBLz1CxrId5WtTEHhgys/IyjuzC9Jjv6DQ0Bp1Ox4tt6mvPZ8NtcN64noz94EsChiwiLiGJca/9\nU3O+xG1buXPnDguWfszAN0YREzU/3/Rli8KYvfBD5i9eRexnq0m9dQuArKy7RMx9FxdnF/NjT6nH\n6Rn4CnOjljM3arnmkwGA7u0amuoXFMW0RRuZM+pF8zRHBz1zx3Sn24gP8R+ymKCeLahYzpP+L/hx\nIyWNTsHRdB8Vw4IJpn2kQhkP1i8cRNc2DTTnMuez4T7sW9+biE8SCAiOJCA4UvPJAED3tg1M9Qte\nwrTF3zJn5H37x6iudBu9Av+hMQS91JyKZT3p2roeAB3fWMr0D7cwfYg/AK++9RkBw5cROOUTbqYa\nmBi+UXM+sP/jtHhQQb7c6FPgqdx/p+aO+bigC1AURa8oSmVFUQqyrAJp2bQmWxJPALD7yDn86nub\np9Wr8Q9OX7jGzVQDd7Oy2XHoDK19awGQfPEa/SaszDevfhNXsv1AMk6ODvyjvBcptw02yFeLLTtM\nTZTdh8/i18Ay35P58x1MpnWzWlbHlCvtzozh3ZgwL1Zzrrx8NfOWdeRc/nzVH54PIPnidfqNX5Fv\nXs3qe7Nt3ykANu84RofmdW2T0acaW3aeNGU8egG/epUtMj7B6YvXuZmaYcqYdI7WTWsAMGf4C8Ss\n38Wv127lZVQqs+3AGVPGxJN0eLqW9nw23AZfDf2YpJOXANOBMiPzruZ8R5MO4PdsSwDqN/Lh5xNH\n802vUasOabdTuXMnE6PRyL1zzWVRYXTt0ZdyFSqaH/uzeow9iduYMGwgC2a/TXp6muZ8LZvWYEui\nCsDuI+cfrN/F++tXk9jvDzFj6beA6dVnVu4rRA93F2bFbGbN1/s158rLZ7t92Le+N53bNGTLspEs\nfqs/nu4uDy7wz+ZrUo0tu342LeuB/aNi/v3j0FlaN61O/I/HGfb+egCqPlmGlNT8r+umDerE4rWJ\nXL6eqjkf2P9x2haMRmOR/zxOBfkjXU1V1TcBVFW9lfvvRx5RFUVZnvvfZ4GTmL42+YiiKC005gXA\ny8M13waRnWPEwcH0VEp5uHLrdt6GnpqWQSlPVwDWb03iblZ2vnnl5Bip+mRZ9n8xifJlPDn88yXb\n58vOuS9f3rTUtExKebo9dIyzkyNL3voXk8LWkZqWqTmXOZ+nKykWNcpXP8/78qVb1u/QA/Wz7Fqk\npmdS2tPNNhk9XEmxeM4P1DDNYh2nZ1LK05X/6+LL1ZtpfLf71H0ZyffY0rnPR3M+G22Dl6+bTl5a\n+FTnjZfbELnmB8350tPS8PDwMv+u1zuQnZXXNaleozYjgvrzxiu9eLZlWzy9SrFlUxyly5TF79lW\n+eal1G9E0NCxfLBoJU9WqsKnK5Zozmeqn+U2aLl+Xe6rn2n9phnucDs9E093F9bMfpUZS74B4Nyl\nG+w5el5zpgfzad+HHRz07D16jtCFcfgPiuDML9eZGqy9w+Ll7pK/ftlG6/VLv2Pe/rKzc4h5sw9h\nY1/ks80HzY95oqwH7f1q8fEm251U2ftxWjyoICcERkVRzH1gRVHqAX/0EqZG7n9nAS+oqvos0Al4\nv1Ap75OaloGXe95BXa/Tma8n3UrLwNMj7wz8/gPPw5y//DuNe73Hsi+3875F+1xTPg+LfHp9/nzu\nlvlcSEk1PHSMT91K1Kr6BBFTXubjOQOoV+NJPhjfS3u+2xl4WdQoX/1uZ+BpUVsvd1dSUq2fjefk\nGC0e60JKarrmfHBvHTvnZdTrrNfQ3VTD17r68c9navNtZBA+dZ5i+bS+/KOc54MZ/2B7KHg+222D\nffybEjGlLz1Hx3DtpvZX4O4eHhgsXsnnGHNwcDS1YM+cOsnuxG189N9NfPTfr7n5+w22bd3M5o3r\n2b93JxOHB5F8SmXezKncuH6Nlm07UqeeqR3fsm1HTv98QnM+0/ZuZRtMy3zIPmKqX5WKpflm8Rus\n+Xofn3+rvfX+6Hza9+Hs7Bw2bE3iwPELAGzYmkSTelW050vPxMsiQ/794776uTvn2/4Gz1yLT2AY\n0ZN74u5quvzXs0MjPt9yKN++ojmjnR+nbSHHWPQ/j1NBTgjGA1sURdmrKMpe4FtgbAHnn62q6s8A\nqqpeKuDy/lDioTMEtDJdB27eqBpHTv1qnnbizBVqez9B2VLuODk60Mq3JruSzlqd13/DgqjlXQGA\n2+mZ5ORov1El8WAyAa1MB9Dmjatz5FTe2eyJM5epXdUiX7Pa7Eo689Axe4+ex6/vbAKCI3ll8kec\nOHPZJpcOTPXLXVajavnznb0/X61H1u+gepE2frUBeL5lA7YfSNacDyDx8HkCnlNMGRt6c+T0FYuM\nV6ldpTxlvdxMGZtUZ9eRC/gPW8bzw5cRMGI5ST//StC7/+XKjdscPPkrbXxN56jPP1eX7YesP58C\n57PhNtjvBT/eeLkNAUMWcfaX61Yf92c0aOzLnp0/AXD8SBI1atYxT3P39MTZxQVnF1ccHBwoU7Yc\nqam3+GDRSj6IWsHcqOXUrK0w/s1ZlCtfgTfHhqAeOwzAwX27qKNov1afeOgsAS1N17SbN6rKkdOX\nzdNM9atA2VK567dpTXYdPkvFcp7ERwbzZtRGVsfv0ZzhkflstA8DxC8K4emGphs6OzSvaz450JQv\n6RwBz5kuz5n2D4v6nf2N2t4W+0fTGuw6fJ7+nZsy/pV2AKRn3CUnx2g+Aej4dG02J57UnCtfRjs/\nTosHFeS7DL5TFKUq0AR4Iffna8DzEcNKK4qyD/BQFCUI030I84Fz2iNDXMJhOj6rkLB8JDqdjuAZ\n/yEwoBke7i6sWJfIpAVxxEcOQafXsXrDLi5dTbE6r/kffU/M9H9x524W6Rl3Gfqu9q9piEtIomML\nhYSVY9DpIHj6pwR29jPli93BpLD1xC8KQafXszpuJ5eupjx0TFGJS0gy1W/FaNOyZqwx5XNzNtUv\nbB3xUSGm+uXms2bygvVEv9kPZycHTpy5Quz3B60+9k9l/OEYHZ+pTcKSYNM6nvUlgf4+eLi5sGLD\nHiZFfk38ggHodDpWb9zHJYt7Bh7IGLWJ6Ek9TRnPXiU24Yj2fDbaBvV6HfPH9+TC5Zt89oHpa0O2\n7TvNzA+/0ZSvZduOHNiTyNg3XsVoNDI29B0SNm/CYEiny0t96PJSH8YPfQ1HRyeequyNf5eXrM5r\n+Pg3WbxwDg4OjpQtX56RE9/SlA0g7n9H6PhsXRKWDTdtg+98TmCAr2kbXL+LSQvjiY8wrfvV8bu5\ndPUW88a+RJlSbkx53Z8pr5tuiHtpdAwZFjeQ2oot9+GRs78gbGIf7mZlc+X6LYbNtMEx5t7+sXSI\nxf7RBA93Z1bE7WFSxCbiFw401e8r0/4R97+jfDi1D1uiB+Pk6MCE8I1k3DHVrk7VCpy5dOMPlvon\nM9r5cdoWStpHF+v+6AkpilIDGAIMBMpgugywWFXVq38wzgXTSUQ6pvsIXgeWq6pakDumjG5PjynA\nw4qHYe8C3JqNLO4YVhn2R+DmN6q4Y1hl2BeOW6upxR3DKsP2WQDY6zZo2Gt662ny1WJ9s88j1XzC\nFbfm44s7hlWG3fPsfx9uGVrcMawy7HgPsPt9xDZvy3qEVz49VORnBB//u0mRP497HvXBRD2BN4Bm\nwDrg/4AYVVXfKciMVVXNBHZb/C/tdyIJIYQQdqKENQgeecngS+C/wHOqqp4CUBRFLtwIIYQQJdCj\nTgh8gAHAT4qinAX+8wePF0IIIf42Sto9BI/6pMIjqqqOByoDs4H2wD8URdmoKEoXa+OEEEII8ddT\nkHcZZANxQJyiKE8Ar2A6QdhUxNmEEEIIu/W4PyegqP2pSwC57ywIy/0RQgghRAkh9wQIIYQQhfC3\nuYdACCGEEH8f0iEQQgghCqFk9QekQyCEEEIIpEMghBBCFEqO3EMghBBCiJJGOgRCCCFEIZSwBoGc\nEAghhBCFIW87FEIIIUSJIx0CIYQQohBKWIMAnZ22POwylBBCiL8MXVEvoNfyfUX+tyo2yM/q81AU\nRQ9EA02ATGCQqqqnHvK4D4EbqqpOftSy5JKBEEIIUQg5RmOR//yBHoCrqqrPAZOB+fc/QFGUIUDj\ngjwfu71k4NZsZHFHsMqwPwI33+HFHcMqw4EoqZ8GhgNRgP1ug4b9EQC4+Y0q5iTWGfaFk5FV3Cms\nc3XE7rdBe93+wGIbtNMa3tuH/wZaA98AqKq6U1GUpy0nKorSEngWWArU+6OZSYdACCGEKASjseh/\n/kApIMXi92xFURwBFEV5CngbKPBZm912CIQQQgjxSLcAL4vf9aqq3uvN9QUqAJuAJwF3RVFOqKr6\nkbWZyQmBEEIIUQh2cFP+duBF4AtFUVoAh+9NUFU1AogAUBRlAFDvUScDICcEQgghxF/VOsBfUZQd\nmN5VMVBRlH8BnqqqfvhnZyYnBEIIIUQh5BRzg0BV1Rzgjfv+94mHPO6jgsxPbioUQgghhHQIhBBC\niMIwlrDP0JMOgRBCCCGkQyCEEEIURvG/ycC2pEMghBBCCOkQCCGEEIVhB59DYFPSIRBCCCGEdAiE\nEEKIwijuzyGwNekQCCGEEOKv2SHQ6XSET+mLT93KZN7JIuTd/5B84Zp5epe2jQgdHEBWdg6r4nay\ncl3iH44J7OxHSL+2tB+wwDb5QgPzlvXOpw/mC37BlG99IivX7fjDMXPH9eLkud9YtvYn2+SzUf2a\nKFWIDQ/m1PmrAMSs/Ym1mw/YJqPUUFu+yX3xqVspd1mfkXzRIl+bhoQO7kxWdjarNuzKy/eQMU3r\nVSFyystk3s0iSf2FcfNiNV87zcnJYda70zmpqjg7O/P2jJlUrVbNPD1+w3pWrVyOp6cX3Xv0pFfv\nvgAsj1nK/xK2cvfuXV7u159evfty/NhR3p3xNs7Ozij16jNpylT0em2vdWy5/dX0rkDMjFcwGo0c\nPf0ro2d/obl+9r79mTPa8T5sC3IPQSEoilJBURSdrebXvUNjXJ2daD9gAdMi45kzpqd5mqOjnrnj\netJtaDT+gyII6tWSiuW8HjmmiVKF13q0QKezTcTuHXxwdXak/WvzmRYRx5yxve7L15tuIVH4By0k\nqHer3HwPH1OhrCfro0Lo2q6xTbKZ8tmufr71vYn4JIGA4EgCgiNtciAxZZQaasrXvjGuLo60H7gw\nd1k9Hsw3LBr/wZEE9czNZ2VM1NRAJsyPpdOgCFJuGwjs7Kc539bvv+NO5h0+XvM5o8aMY/4Hc8zT\nfv/9BtGRESxf+TErVn3Cpq/i+eWXi+zZvYuDBw6w6pP/sOKjj7ly+TIA70yfxsTJoXz08Rq8PD3Z\ntDFecz5bbn/vj+vN9EVf0SloITqdjhfba98O7X37M2W0731YPKhIOgSKogwEvIGvgDVABqavXhyq\nqup3Wuffsmkttuw4DsDuw2fxa+BtnlavxpOcvnCNm6kGAHYcTKZ1s1o861PjoWPKlXZnxvBuTJgX\nS/S0/lqjmfL53p+v6n35rublO3Ca1s1q82yT+/OZxni4uTBrySaeb9XQJtnAtvXzre9N3eoV6da+\nMafOX2XCvFhup2dqzyg11JivZt6yjpzLn6+6lXyNqz90TOWKZdiZdBaAxENn6NauMZ99vVdTvgP7\n99GydRsAfJo05ejRI+ZpFy9cpK6iULpMGQAaNmpM0qFDnFRPUKduXcaMHMbt27cZO34iAFcuX6Gp\nbzMAmjZrRsLW7+n24kua8tly+2tW35tt+34GYPP2o/yzRX02JCRpy2fn2x/Y/z5sCyWsQVBkHYKh\nwHzgA6C7qqpNgfbAbFvM3MvDlZTbBvPv2dk5ODiYnkopD1duWUxLTcuklKfbQ8c4Ozmy5K1/MSls\nHalp2neAQuVLz6SUl6vVMecuXWfPkXM2y/an8z2ifg4OevYePUfowjj8B0Vw5pfrTA3u/PgzSg0f\nzOfpSsrtjLxl5Rjz8nneX78MSnm6Wh1z9pfrtG5WCzC1eT3cnDXnS0u7jZeXp/l3B70DWVmmr3Gv\nVq0ap0+d4vq1axgMBnbvSsRgSOfm779z9OgR5oWFM+3tGUyZNB6j0UgVb2/27tkNwA8JCRgMs+Or\nugAAIABJREFUhocu88+w5fZn2XlMTcuktKfr480n+3CRyTEai/zncSqqE4K7qqqmAalAMoCqqpfA\nNh/8nJqWgZdH3k6l1+vJzs4B4FZaBp7uLuZpXh4upKQaHjrGp24lalV9gogpL/PxnAHUq/EkH4zP\na2tpymeRQa/X5c9nkcPL3SKflTG2Zqv6ZWfnsGFrEgeOXwBgw9YkmtSrYruMUsPC57udgZeHRS10\nFvW7nYGnu2X9XE35rIwJnrGGCQP92bR4GFdvpHL9ZprmfB4enqSl5c0nx5iDo6OpYVmqdGnGT5rC\n2NEjmDxhLPXrN6Rs2bKULlOGlq1a4+TsTPUaNXFxduHGjRu8M/M9lscsZfDrr1GufHnKlimrOZ8t\nt7+cnLxt8N62YJN8drz9mTPa8T4sHlRUJwQbFEWJA44CXymKMkZRlG+BrbaYeeLBZAJaNQCgeePq\nHDl1yTztxJnL1K76BGVLuePk6ECrZrXZlXTmoWP2Hj2PX9/ZBARH8srkjzhx5jIT5sXaJl/rhgXP\nd+jMI8fYmq3qBxC/KISnG5raeh2a1zUfWGySUWpY+HyHzuQtq1G1/PnO3p+vFruSzlod80LrBgx8\nczVdQhZRvrQH3+9SNefz9W3GTz/+CEDSoYPUqVPXPC0rK4sTx4/x0cdr+CAsnDNnkmnq2wzfZn7s\n+GkbRqOR3367gsFgoEyZMvz4ww/MnjuPmBWruHnzJi1attKcz5bb38ETF2njVweA51s1ZPuB07bJ\nZ8fbnzmjHe/DtmA0Fv3P41Qk9xCoqjpHUZR2QABwHqgIRKiqutEW849LSKJjC4WElWPQ6SB4+qcE\ndvbDw92FFbE7mBS2nvhFIej0elbH7eTS1ZSHjikqcVsP0bFFPRI+GotOpyP47U8I7Px0br7tTJof\nS3z0MHQ6XV6+h4wpsnw2rN/I2V8QNrEPd7OyuXL9FsNmfm6bjFJD7fmeVUhYMdq0rBlrTPncnFmx\nLpFJYeuIjwpBp9flz3ffGIBT56+yafEwDBl3+WHvz3y7/ZjmfB07+ZOYuJ1X/90Po9HIOzPfY9NX\n8aSnp9Pn5UAAAvv0xMXFhVdfG0jZsuVo174D+/fu4d+BfcgxGpny5ls4ODhQtVo1gl8fgKubG880\nf5Y2bdtpzmfL7W9y2Dqi3+qPs5MjJ5IvE/ud9pv27H37A/vfh8WDdHb6tgmjW7ORxZ3BKsP+CNx8\nhxd3DKsMB6KQ+hWe4UAUgN3W0LA/AgA3v1HFnMQ6w75wMrKKO4V1ro7Y/TZor9sfWGyDdlrD3H3Y\nZu9ss6ZD+I4i/wOaMKplkT+Pe+SDiYQQQgjx1/xgIiGEEKK42WeDvfCkQyCEEEII6RAIIYQQhWGn\n9+AVmnQIhBBCCCEdAiGEEKIwSliDQDoEQgghhJAOgRBCCFEocg+BEEIIIUoc6RAIIYQQhSAdAiGE\nEEKUONIhEEIIIQqhhDUIpEMghBBCCOkQCCGEEIUi9xAIIYQQosTR2ekZjl2GEkII8ZehK+oFPPf+\nj0X+typxUtsifx73yCUDIYQQohDs9AV1odntCYGb36jijmCVYV84br7DizuGVYYDUbg9Paa4Y1hl\n2LvA7usH2G0NDXsXANh9De093/W0rOKOYVV5D0fcmo0s7hhWGfZHAODWamoxJ3k4w/ZZxR3hL8lu\nTwiEEEIIe1bCGgRyU6EQQgghpEMghBBCFEpJu4dAOgRCCCGEkA6BEEIIURglrEEgHQIhhBBCSIdA\nCCGEKBS5h0AIIYQQJY50CIQQQohCKGENAukQCCGEEEI6BEIIIUShyD0EQgghhChxpEMghBBCFEIJ\naxBIh0AIIYQQf9EOgU6nI3xyX3zqViLzThYh735G8sVr5uld2jQkdHBnsrKzWbVhFyvXJVod41O3\nMpGhL5OVncPP534j5N3PNF8X0ul0hIcG4lO3smlZ73xK8gWLfG0bERr8AlnZOaxan8jKdTusjqlX\n80kWvdkfnQ5Onb9KyDtryM7O0Z5vch986lQi824WIe9+/mD9Bj1vyrdhFyvX7zRPe6ZhVWaOfJGA\nIYsAaKJUJnbBIE7lPr+YtdtZu+WgpnzmjH+TGvrUrUTYhN5k5+SQeSeLQW9/ym83bmvPZ6P63TN3\nXC9OnvuNZWt/0pTN1vlqelcgZsYrGI1Gjp7+ldGzv9C8D+fk5DBv9rv8fFLF2dmZKdNmUKVqNfP0\nr7/awJrVK/H09KRL9x682KO3edrRw0lER4SxKOYjANQTx5kwaijeueN79gmkU8ALmvLpdDrCp/TN\nq8W7/3mwfoMDTPWL25l3DHzImCZKFWLDgzl1/ioAMWt/Yu3mA5rymTOO745P7SdNy5uzjuRfbuRl\nbFWP0IEdTBm/2sfK+L3o9TqiJ/WkbtUKGI1GRnwQx7Ezv9G0biUiJ7xE5t0skn7+lXELN9rF9Xt7\nyGBLRdIhUBSlVFHM957u7Rvj6uJI+4ELmRYZz5wxPczTHB31zB3Xk27DovEfHElQz5ZULOdldczU\n4M68F/Mt/wwKx8XZkRdaN9Cer4MPrs6OtH9tPtMi4pgzttd9+XrTLSQK/6CFBPVuZcpnZcw7w1/k\nragNdBy4AICubRtpz9e+kWlZr4czLfIr5ozpnpfPQc/csS/RbfgS/IOjCOr5HBXLeQIw9tWORE8L\nxNU57zzSt14VIj79gYAhiwgYssgmJwPw96rhvHE9GfvBlwQMWURcQhLjXvun9nw2rF+Fsp6sjwqh\na7vGmnMVRb73x/Vm+qKv6BS0EJ1Ox4vttef8MeF77tzJJGbVGkJGjCFiwQfmaTd//52YxZEsilnJ\nomWr+HbTV/x66RcAPvloObPffYs7mZnmx6vHj9Lv/15jUcxHLIr5SPPJAED3Do1xdXai/YAFucez\nnuZp5mPg0Gj8B0UQ1Cv3GGhljG99byI+SSAgOJKA4EibnAwAdG9b37S+hixl2pLNzBnRJS+jg565\nI7vQbcxK/IctI+ilZ6hY1oOureoB0DHkQ6bHfMf0Ic8DEDWpBxPCN9JpaAwptzMI9PexSUaRX1Fd\nMrisKEpQEc2blk1rsmXHcQB2HzmHXwNv87R61Z/k9IVr3Ew1cDcrmx0Hk2ndrJbVMQfVi5Qt5Q6A\np7sLd7OytefzrZW3rMNn8WtQNS9fjSc5feFqXr4Dp2ndrLbVMf3GL2P7/tM4OTrwj/KlSLmdoT1f\n05psSTxhWtaRc/jVt6hfjX/kr9+hM7T2rQVA8sVr9JuwMt+8fOt707l1A7Z8OJzF0wLxdHfRnA/+\nXjV8NfRjkk5eAkwHyozMu9rz2bB+Hm4uzFqyiTUb92jOVRT5mtX3Ztu+nwHYvP0oHZ6tpznfoYP7\nebZlawAa+TThxLGj5mmXfrlA7boKpUqXQa/XU79hI44cPgRAZW9vZs8Lzzcv9fgxdmz7gZCgV3lv\nxjTS0tI052vZ9P5aWG5/1o6BDx/jW9+bzm0asmXZSBa/1d92+7BPNbbsPGla3tEL+NWrnJex+hOc\nvnidm6kZpoxJ52jdtAbx244zbO56AKo+WYaU2wYAKj9Rip1HzgOQePg8LZtUt0lGrYzGov95nIrq\nhOAQ4KsoylZFUdrZeuZenq75DurZOUYcHExPpZSnK7dyNyKA1PQMSnm6Wh1z+vxV5k/oxcEvQ/lH\neS9+3HdKez4PV/OGDJCdnZOXz+P+fJmU8nK1OiYnx0jVp8qy/8uplC/ryeGTv9g+n2X9PFy5ZVGn\n1DRT/QDWb0164IRp79HzhIZvwD84ijO/XGfq4ADN+R6asQTX8PL1WwC08KnOGy+3IXLND7bPp6F+\n5y5dZ8+Rc5ozFVU+nU6X99i0TErn1lqL9LQ0PD29zL87OOjJysoCoErVapw5fYob16+RYTCwb/cu\nMgymXB3++TyOjvmvxNZv2Jjho8ezePlqKlWuwooPozXn+1P1S8uklKeb1TF7j54jdGEc/oMiTPtw\ncGfN+cwZ0/I6JQ9kTLPYR9IzzftIdnYOMW/2JmxMNz7bbDrROnvpBq2bVgdMlxo8XJ1sklHkV1Qn\nBAZVVYcDE4GRiqIcVhRloaIoI20x89TbGXh55J3F6nU68zXhW7cz8HTPOyB4ubuSkmqwOuaD8b3o\nNCiCpr3f49Ov9uS7/FDofGkZeFmcZev1FvnSMvD0sMznYsr3iDHnf/2dxi+9w7K123h/XF5rVVu+\nvAz56peWgadFnUwHEeuvqDckJHHgxMXcfx+miVLZ6mP/fMa/Rw0B+vg3JWJKX3qOjuHaTe2vIG1d\nP1uzZb6cnLyMXh6mx2rl7uFBusUr+Zwco/kPfalSpRk1bhKhE0bzVugE6tarT+kyZa3Oq13Hf1Kv\nQUPzv0+eOK45X2paBl4WNdLr9fnr5265/VnU7yFjNmxN4sDxCwBs2JpEk3pVNOczZ3R3tlieznpG\n9/zrbfDML/Hpt4DoST1wd3Ui+L1YJrzSjk3hr3P199tcT0m3SUatjEZjkf88TkV1QqADUFV1r6qq\nvYHWwPeA8yNHFVDioTMEtDJd62/eqBpHTl0yTztx9jK1qz5B2VLuODk60KpZLXYlnbU65vdb6aTm\nnqn+eu2W+fKBpnwHkwlobToANG9cPX++M/fnq82uQ2esjvnvwiHUqvoEALfTMsnJ0b6BmGpR37Ss\nRtU4cupXi3xXqO1tkc+3JruSzlqdV3zUGzzd0NS67dC8rvnkQHPGv1EN+73gxxsvtyFgyCLO/nJd\nczawbf2Kgi3zHTxxkTZ+dQB4vlVDth84rTmfT1NfErf/CMCRpEPUql3HPC0rKwv1xHEWL/+Yme+H\nce7sGXya+Fqd15hhwRw7kgTA3t27qFdf+31KiQeT845nBalf0hmrY+IXheTfh3NPDjRnPHyegOcU\n0/IaenPk9JW8jGevUrtKecp6uZkyNqnOriMX6B/QlPGvtAUgPeMuOTlGcnKMvPCcwsAZX9Bl1ArK\nl3bn+z3aO7m2UNJOCIrqXQYfWf6iqmoKEJ/7o1lcQhIdn1VIWDEanQ6CZ6whsLMfHm7OrFiXyKSw\ndcRHhaDT61gdt5NLV1MeOgZg6Lufsfq918jKzuHO3WyGzvxMe76th+jYoh4JH41Fp9MR/PYnBHZ+\nGg93F1bEbmfS/Fjio4eh01nke8gYgPkrNxMz4/+4czeb9Iw7DH1njfZ8CYdNtVg+0rSsGf8hMKCZ\nKd+6RCYtiCM+coipfht2celqitV5jZy9lrCJvbiblc2V67cYNusLzfng71NDvV7H/PE9uXD5Jp99\nMBCAbftOM/PDb7Tls2H9ioIt800OW0f0W/1xdnLkRPJlYr/TflNcuw6d2LMzkeAB/8ZoNDJ1+kw2\nf/0V6enp9Oj9MgAD/tUHZ2cX+r/yGmXKWu8QTJjyFmFzZ+Ho6ES58hWY/OZ0zfniEpLo2EIhYeUY\n0/Fs+qemY6C7CytidzApbD3xi0LQ6fX5j4H3jQEYOfsLwib2yduHZ36uOR9A3A/H6PhMbRKWBJvW\n16wvCfT3wcPNhRUb9jAp8mviFwwwreON+7h07RZxPxzlw9DebFk0CCdHByaEbyLjThanLl5nU0QQ\nhow7/LD/DN8mnrRJRpGfzk7fNmF08xtV3BmsMuwLx813eHHHsMpwIAq3p8cUdwyrDHsX2H39ALut\noWGv6d0S9l5De893PS2ruGNYVd7DEbdmNrnCWiQM+yMAcGs1tZiTPJxh+yzI7VQXpUZvbinyP6BH\nZvoX+fO4Rz6YSAghhBB/zQ8mEkIIIYqbnXbYC006BEIIIYSQDoEQQghRGCWsQSAdAiGEEEJIh0AI\nIYQoFFt8pok9kQ6BEEIIIaRDIIQQQhSG3EMghBBCiBJHOgRCCCFEIcjnEAghhBCixJEOgRBCCFEI\nJaxBIB0CIYQQQkiHQAghhCgUuYdACCGEECWOzk7PcOwylBBCiL8MXVEvoNa4r4v8b9Xp+S8U+fO4\nRzoEQgghhLDfewjcmo0s7ghWGfZH4NZ8fHHHsMqwex5ufqOKO4ZVhn3huD03ubhjWGVInAPY7zZo\n2B8BYPc1tPtt0E7XL5jW8YUbmcUdwyrvci4AuD0ztpiTPJxhT9hjWY6ddtgLzW5PCIQQQgh7VtJO\nCOSSgRBCCCGkQyCEEEIUSslqEEiHQAghhBDSIRBCCCEKRe4hEEIIIUSJIx0CIYQQohCkQyCEEEKI\nEkc6BEIIIUQhSIdACCGEECWOdAiEEEKIQpAOgRBCCCFKHOkQCCGEEIVRshoE0iEQQgghxF+0Q6DT\n6Qif0hefupXJvJNFyLv/IfnCNfP0Lm0bETo4gKzsHFbF7WTlukSrY5ooVYgND+bU+asAxKz9ibWb\nD2jPN6kXPnWeIvNONiGzviD54vW8fK0bEDrIn6zsbFZt2MPKuF04OuhZOi2QapXK4uLkyJwV37Fx\n2zFqVilPzFv9MGLk6OnLjJ67TvN1K51OR/jkvvjUrZRbi89IvmhRvzYNCR3cOTffLlauSzRPe6ZR\nNWaOeJGAIVEA1KxSgZgZ/8ZoNHL09K+MnrPWJtfVdDod4RNewqf2U2TezSZk9pf31bA+oQM7mtbx\nV3tZuWEPer2O6Cm9qVu1AkYjjJi7jmPJV8xj5o7qxsnzV1m2bpdt8tloG3yirCeLpvWnbCk3HPR6\ngt76hDMW66PQ+ey9fn9yG7Q2pmm9KkROeZnMu1kkqb8wbl6sbfYRG63fewI7+xHSry3tByzQlA0g\nJyeHiA9mcfqUipOTM+OmTKeyd1Xz9O+/3ch/16xC7+BA52496N4rEIA1q5aR+NP/yLp7l+69Anmh\ney9OnTzBwrnv4uDgSBXvaowLnY5er/21ouk42BufOpXIvJtFyMwv7lvHDQgd9DxZWTmsit/NyvU7\nzdOeaViVmSO6EfBGNABN6lYmdsEgTl3IPU5/uYO1Ww5qzqiV3ENgB7p3aIyrsxPtByxgWmQ8c8b0\nNE9zdNQzd1xPug2Nxn9QBEG9WlKxnJfVMb71vYn4JIGA4EgCgiM1nwwAdG/XEFdnR9oHRTFt0Ubm\njHoxL5+DnrljutNtxIf4D1lMUM8WVCznSf8X/LiRkkan4Gi6j4phwQRTvvdHd2f6km/oFByNTqfj\nxXYNtedr3xhXF0faD1yYW4seefnu1W9YNP6DIwnqaaofwNhXOxI9rR+uLk7mx78/tgfTozfSaVAE\nOnS82L6x5nwA3ds2MK2v4MVMi/6aOSO65mV00DN3VFe6jV6B/9APCXqpORXLetK1dX0AOg5ZwvSl\nm5k+JACACmU8WB820DzdJvlsuA3OGvUSn3+9F/9BEUyP3ohSvaL2fPZev0Jsg9bGRE0NZML8WDoN\niiDltoHAzn7a89lw/QI0UarwWo8W6HQ6zdkAtv+4lTt3MomM+YRBQ0exJHJevulLI+czNyKG8KWr\nWbtmNam3bnFw/x6OHT5I+NLVhEWv5LffLgPw8fIlvPL6G4QvXcXdu3fYtf1Hm2Ts3r6RaX0FRTAt\naiNzRnc3TzMdB3vQbfhS/IcsMh8HAca+0oHoNwNxdc47zvjWr0LEmv8R8EY0AW9E28XJQEn0WE4I\nFEVxVhTFzVbza9m0Flt2HAdg9+Gz+DXwNk+rV+NJTl+4xs1UA3ezstlxMJnWzWpZHeNb35vObRqy\nZdlIFr/VH093Fxvkq8GWRNW0rCPn8atvme8fnL5oke/QGVr71iT2+0PMWPotYDqzzsrOAaBZvSps\n238agM07TtDhmTo2yFczrxZHzuWvX/WH1w8g+eJ1+o1fkW9ezep7s23fqdx8x+jQvK7mfAAtm1Rn\ny87cGh69gF/9yhYZK3L64vW8jEnnaO1bg/gfjzFsTiwAVZ8qQ8ptAwAebs7MWvYda77RfrJnzmfD\nbfC5pjWoXLEMGxcPo98LT/Pj3lPa89l9/f78NmhtTOWKZdiZdBaAxENnaNm0pg3y2W79livtzozh\n3ZgwL1ZzrnuOHDrAMy1aAdCgURNOHj+Wb3qN2nVJS0vlzp1MjBjR6WDvzh3UqFWHtyeP5s0JI2jR\nqh0AtevWI/VWCkajEUN6Gg6Otmkct2xSgy07TgC56+tRx8GDZ2jta3Gcmbgy37x861Whc6sGbFk6\njMVvBtrkOG0LRqOxyH8epyI5IVAUpa6iKGsVRVmjKEoL4AhwVFGUQFvM38vD1XywAsjOzsHBwfRU\nSnm4cstiWmpaJqU83ayO2Xv0HKEL4/AfFMGZX64zNbizjfJl5C0rxzKfC7csppnyuZJmuMPt9Ew8\n3V1YM/tVZiz5BgDLFxSp6ZmU9nTVns/z/nzGvHye99UvPYNSuctcv/UQd7Oy883L8hWPKZ9tzvse\nqGG20XoN0zMp5eGa+7gcYqb1JWxsdz771vQq4tyvv7Pn2AWb5MqfzzbbYLWnyvN7ajpdQxZx4fLv\njBvQyUb57Lh+hdgGrY05+8t180lrl7aN8HBz1p7PRuvX2cmRJW/9i0lh60hNy9Sc6570tNt4eHqa\nf9c76MnOyjL/XqNmbYYO6Megf/WkRau2eHqVIiXld06eOMZbs+YzeuKbzJ4+GaPRSGXvqiwKm8Pr\n/V7i9xvXadrsGZtk9PJwJSXN2nHw/nWcmXecSUh64Diz99h5QiPi8R+yyHScHvy8TTKK/IqqQxAD\nLAG+BL4COgCNgdG2mHlqWgZeHnl/GPV6Pdm5r6hvpWXkO3v08nAhJdVgdcyGrUkcOG462G3YmkST\nelVslC8vg16ns8iX+ZB8pp2mSsXSfLP4DdZ8vY/PvzW9GsvJyTtD9HLPe6ymfLcfke92Bp7ueXXy\ncnclJdXwwDzueTBfuuZ88JAa6h9RQ3eXfAfiwe/+F5+X5xE9uRfurnltR1uy5TZ4PSWNjT8cBmDT\nj0doZvFqVFs+O65fIbZBa2OCZ6xhwkB/Ni0extUbqVy/maY9n43Wr0/dStSq+gQRU17m4zkDqFfj\nST4Y30tzPncPT9LT8vY1Y06O+ZV98qmT7Nr+Ix/Hfs0nsd9w8/cb/PD9ZkqVLsPTz7bEyckJ72o1\ncHZ24ebvN4he+D4LlnzEys834P9Cd5ZEzLO22D8lNS0DL3drx8H717HLI48zGxIOc+DERdO//3eY\nJkplq499nIq7Q6Aoil5RlCWKoiQqivI/RVFq3ze9v6IouxRF2Z77uEf+zS+qEwJHVVW/A2KB66qq\n/qKqahpw1xYzTzyYTECrBgA0b1ydI6cumaedOHOZ2lWfoGwpd5wcHWjVrDa7ks5YHRO/KISnG5pu\nxunQvK755EBTvkNnCWhZz7SsRlU5cvqyRb4r1PauQNlSbqZ8TWuy6/BZKpbzJD4ymDejNrI6fo/5\n8QdPXqJN7quf51vWY/vBZBvkO5NXi0bV8tfv7P31q8Wu3HbswxxUL9LGr3ZuvgZsP6A9H0Bi0jkC\nnsutYUPv/DU8+9t9NazOriPn6d/Zl/GvtgcgPeMuOUYjOUXUcrPlNph4MJmA1qZ7Q1o3q8Xx5Mto\nZff1K8Q2aG3MC60bMPDN1XQJWUT50h58v0vVns9G63fv0fP49Z1NQHAkr0z+iBNnLtvk0kFDn6bs\nTtwGwLEjh6hRK+9SooeHJ84urri4uOLg4ECZsuVITb1FYx9f9uzcjtFo5NrV38gwGChVugxeXqVx\n9zB1G8pXeILU1Fua80HucbCV6b6T5o2qceT0r+ZpecfB3Br61mTX4XNW5xUfOYSnG+Qep5+pw4Hj\nF22SsQToAbiqqvocMBmYf29C7mX6mUAHVVVbAaWBbo+aWVG9y+Csoiif5c7/tqIos4AU4NdHDyuY\nuIQkOrZQSFg5Bp0Ogqd/SmBnPzzcXVgRu4NJYeuJXxSCTq9nddxOLl1NeegYgJGzvyBsYh/uZmVz\n5foths38XHu+/x2h47N1SVg23LSsdz4nMMAXDzdnVqzfxaSF8cRHBKPT6Vgdv5tLV28xb+xLlCnl\nxpTX/Znyuj8AL42OYXL4BqJD++Ls5MCJM78RuzVJe76EJDo+q5CwYrQp34w1pvq5ObNiXSKTwtYR\nHxWCTq8z18+ayQvWE/1mv9x8V4j93jY3+8T9cJSOzWuT8GEIOiB41loCn2+Ch5sLK+J2MyliI/EL\nXjdl/Govl67eIu5/R/jwzb5siR6Ck6OeCQu/IiMz6w+XVah8NtwGJy9YR/S0/gT3aU3KbQMDQldp\nz/dXqN+f3AYfNgbg1PmrbFo8DEPGXX7Y+zPfbj/2B0svYD4brd+i0LrdP9m/eycjB7+CESMTpr7L\n999uxGAw0K1HH7r16MPoIa/h6OREpcpVCOj6Ek5OTiQd3MewoH9hzMlhxPhQHBwcGBs6nVnTJuLg\n4ICjkxNjp7xtk4xx/ztsOg4uH4EOHcHvfEZgQDM83J1ZsW4nkxbGER9peRy0fpwZOWctYRN65R6n\nUxn23hc2yahZ8b/JoDXwDYCqqjsVRXnaYlom0FJV1XutJEfgkS1mXVHctKAoiiPQBTgJ3AbGADeA\nhbmdgj9idGs20ua5bMWwPwK35uOLO4ZVht3zcPMbVdwxrDLsC8ftucnFHcMqQ+IcAOx1GzTsjwCw\n+xra/TZop+sXTOv4wg3b3XNga97lTJcC3J4ZW8xJHs6wJwzANm/peIRKQ2KL/JTg0tJeVp+HoijL\ngC9VVf069/fzQE1VVbPue9wITH+Tu6iqajVzkXQIcsNssPhf44piOUIIIURxsYPPIbgFeFn8rrc8\nGci9Z2AuUBfo/aiTAfiLfg6BEEIIUdyK+6ZCYDumV/7kvqPv8H3TlwKuQA+LSwdW/SU/qVAIIYQQ\nrAP8FUXZgekSyUBFUf4FeAJ7gSBgG7BVURSAcFVV11mbmZwQCCGEEIVQ3JcMVFXNAd6473+fsPj3\nn7oKIJcMhBBCCCEdAiGEEKIwirtDYGvSIRBCCCGEdAiEEEKIQilZDQLpEAghhBBCOgRCCCFEocg9\nBEIIIYQocaRDIIQQQhSCdAiEEEIIUeJIh0AIIYQoBOkQCCGEEKLEkQ6BEEIIURglq0GuESP7AAAY\nPUlEQVSAzk5bHnYZSgghxF+GrqgXUP7V/xT536rrq/sX+fO4x247BG7NRhZ3BKsM+yNw8x1e3DGs\nMhyIsv/6NR9f3DGsMuyeB4Bbq6nFnOThDNtnAbKPaGE4EIVbi0nFHcMqw873cfMbVdwxrDLsCwfg\n0s07xZzk4SqVcX4sy7HTF9SFJvcQCCGEEMJ+OwRCCCGEPZMOgRBCCCFKHOkQCCGEEIVQ0joEckIg\nhBBCFEJJOyGQSwZCCCGEkA6BEEIIUSglq0EgHQIhhBBCSIdACCGEKBS5h0AIIYQQJY50CIQQQohC\nkA6BEEIIIUoc6RAIIYQQhSAdAiGEEEKUOH/JDoFOpyN8Sl986lYm804WIe/+h+QL18zTu7RtROjg\nALKyc1gVt5OV6xKtjlk9+zX+Ub4UANUqlWP34bO8OmWV9nyhgXnLeufTB/MFv2DKtz6Rlet2WB3j\nU7cyYZP6kp1jJPNOFoOmrea3G6na89mofvcEdvYjpF9b2g9YoClbvoyTeuFT5yky72QTMusLki9e\nz8vYugGhg/zJys5m1YY9rIzbZZ72TMOqzBzelYCQxQA0VSoTObk3mXezSDp5iXHz4zSf2et0OsLH\nd8en9pOmesxZR/IvN/LytapH6MAOphp+tY+V8XvN054o48GOFUPpOnolJ89fo2blcsRM7Y0ROJp8\nhdHz422Tz0bruIlShdjwYE6dvwpAzNqfWLv5gPZ8NtpHanpXIGbGKxiNRo6e/pXRs7+wTf0m9DBt\nf3ezCHnvy/u2v/qEvv7P3PW7l5Vxu3F00LP0zb5Ue6osLk6OzPnoezZuO45PnaeInNSLrOxsfj5/\njZD3vrRNvsl98albKXddfUbyRYv6tWlI6ODOufvHLlauSzRPe6ZRNWaOeJGAIVH55jl3bE9OnvuN\nZV9u15TtnpycHBbOncnpn1WcnJ2ZEDqDyt5VzdO3fPMV/12zGr1ezwsv9uSl3oFkZd1l9oypXPn1\nEnq9A+ND36Zq9ZqcTT7N/NkzMGKkindVJoTOwMGx+P98SYfADnTv0BhXZyfaD1jAtMh45ozpaZ7m\n6Khn7riedBsajf+gCIJ6taRiOS+rY16dsoqA4EgCxy3jZqqBifPX2SCfD67OjrR/bT7TIuKYM7bX\nffl60y0kCv+ghQT1bpWb7+Fj5k3sw9j3/0vA4HDith5k3EB/G+SzXf0AmihVeK1HC3Q6neZs5ozt\nGprqERTFtEUbmTPqxbyMDnrmjulOtxEf4j9kMUE9W1CxnCcAY19pT/TUvrg65x0sokL7MCEsjk7B\n0aTcziAwwFd7vrb1TfmGLGXaks3MGdElf76RXeg2ZiX+w5YR9NIzVCzrYZ4WNbEHhsws8+PfH9mF\n6THf0WloDDqdjhfb1Neez4br2Le+NxGfJBAQHElAcKTmkwFTPtvtI++P6830RV/RKWihqX7tG2vP\n164Bri6OtB8czbRF3zBnZNe8fA565o7qRrdRy/EPWUrQS82pWM6T/p2bcSMlnU5vLKH7mOUsGNcD\ngKlBnXhv+Xf8c8gSXJwdeaFVPe352jc25Ru4MHdd9cjLd2/9DovGf3AkQT1N6xdg7KsdiZ7WD1cX\nJ/PjK5TxYH3EELq2a6Q5l6WfftjKnTuZLFr+KcFDRxMd/kG+6Usi5jMvMobImI/5Ys0qUm+lsHP7\nNrKzs4la9gmvBg1h2eJIAJYtDmfQ0JFExXwMwI6ffrBpVmFS5CcEiqLY7q9ErpZNa7Flx3EAdh8+\ni9//t3fnUVKU5x7Hvz0DDLOBaESDoiLL4xbZFQEVcUGJesUbRRP3BTfgaq4R1OgVl4O5EXdwQ9yJ\nxogLioLLGA0CRhQGNDwsAi6IQQRmGIZllvxRNUzPKkz30E37+5wzB7qru+rXVV3dbz31dr0Htd06\n7YB2e7Lk6x9YW1jMlpJSPprzJX27ta/3OQA3XT6Qh57/gJU/FMSer2v1ZVW2ioN8qyrzfbaEvt06\n1Pmc80Y+Qf7CbwFokp7Oxk1bYs8Xx/W3a8ssRg09mT/cNSnmXFUztuPtGR4sb/5XdD8wOuMeLPkm\nKuPcpfTtuj8AX36zmrNGVK3w7NW6JTPnLQdgxtxl9O7SLvZ8h+7L2zMXBvk+/5ruB+xVmW+/3Vny\nzWrWFm4M8uUvp2+4zDuHnsRjr8ziu6j3WTfbiw8/WwrAtBkLOaZH+9jzxXEbdz2wLSceeTBvjx/O\nQzefTU5WRuz54riPdDuwLR/OXgTAtOmfc8zhsX/h9u7cjrdnVGzfr+h+wN5R+VqH27fi/beMvl3a\nMem9fEY9OhWACBFKSksBmLNwBa1aZgGQk5XBlpLS2PN12b9yXcxfXnX77lf79oVw/7h2QpV5ZWdl\ncMejbzHxjX/GnCvavLmfclivvgAc9KvOLFzwRZXp+3foRFFRIZs3bYLyciKRCG332Y+y0lLKysoo\nKiqiSVgFGHXnPXTu2oMtW7bw4+rVZOfkxDVrg5XvgL8dqFEaBGbW3szeMrPlwGYzm2lmE81sz3jM\nPze7OevWF2+9XVpaRnp68FJaZDenIGpaYdEmWuRk1vuc3Vvl0O+wTjwzubLsvMPybdhEi9zmdT6n\nooHSq3M7Lh98FA88l7dj89Wz/po1bcLDN/+WEXe/TGHRpphz1cy4sXJ5ZdEZMyiImhZkbA7AK3nz\nanzgLvv2x60NhoFHHkR282bxyRf1mmusw6KofBuCfOcM7MqqtUW88/HiKvOKLqwUbthEy/C1xJwv\nTvvIJ58v54Z7X+X4S+5n6beruXHIiTs230/sI9GVqcKieK2/DNYVRb//yn9y+xYVb2b9hs3kZDVj\n4uhzGPXINACWfP0DY645lTnP/y977JrDB59+GXu+nOr7R1S+nOrrb2Pl/vHe3Br7x/IVP/LP+ctj\nzlTdhqKiKl/caWlplJZUVsbate/AZecP5sKzT6NXn6PJyW1BZlYWK79bwflnnsqY0bdw+uDfAZCe\nns7K71Zw4VmnsW7tGtp3tLjnlcarEIwFhrv7vsCRQB4wBng8HjMvLNpIbnblTp+WlkZpaRkABUUb\nqxzB5GZnsK6wuN7nDDquCy+8NZuysvg0xwqLNpIblSEtLVI1X1SO3KyofHU85zcndOP+G85i0PCH\n+GHN+vjki8P6O7RTG9rvszv3X38mz9x5AQe025M/X1tZ+o09Y9T6iESvw021ZNxYYx4Vhtz6An+4\noD9Txl7GqjXrWb2uKD75siobFjW2cXS+cBuf/+vuHNuzA1MfuJhDO/6Sx286gz12zanyvsvNyqjy\nQR9TvjjtI6+9l89n//oagNfey6dz1NFyTPnitI+UlZXVeC2x59tUf74a2zfYZnu3bslbYy9j4puf\n8sK0OQD8+ZpTOe7yh+ly1hiem/JpldMPDc63vp79Y/1GcrKi11/zuKyT7ZWVnc2GDZX7WllZ2dbz\n/ksWOTOnf8DEl9/iL69MZe2aH3n/3am8+Jen6Xl4b5752+uMf/Yl7hx1Y1BBAPb8ZRuefekNTj39\nTMbd++dal7mjlZeXN/rfjtRYDYKW7r4QwN1nAn3cfTbQKh4znzHnSwb0OQiAw361H/MXr9g6bcHS\nlXTYZ3datciiaZN0+nTrwKz8pfU+p//hxrTpVctZMefre/C255u7tM7nnDWwJ5cPPooBl97Hsm9X\n11xYQ/PFYf198vlXdD9jNAOGPMC5I59kwdKVcTt1MGPuMgb0Dkq/hx2yD/OXrIzK+D0d2v6CVi0y\ng4xd9mfWvGV1zuukvgdy4c0TGXjVI+zWMot3Zy2MPd+8rxhwRHCUctjBbZm/5PvKfMtW0WHv3WiV\nG+brvB+z5n/N8VeN54Sh4xkw7HHyF33Hxbe9yPc/rmfOwu84smtwSuGEIzoxfW7dr2Wb88VxH5k8\n9gp6HByU5485rNPWxkHM+eK0j8xZ8A1Hdu8IwAl9Dmb6Z0tiz5e/jAG9K7Zv9fffv6u+/7q2Y9b8\n5bTeNYfJ91/CH8dO4enXKzuRrinYQGFYUfjuhwJa5WbGnm/u0sptdci+Vdffsurrrz2z8pfFvMzt\ndcihXZn10YcAfDFvLvt36Lh1WnZOLhkZzcnIaE56ejq7tNqVwoICcnNbbK0q5LZoQUlpCaVlpdx4\n7TC++SqoYmRmZZMWx/5KUqmxuml+aWYPA28CJwOfmNmvgdgPzYBX8/Lp38vIe+IaIhEYcstzDD6x\nO9lZGUyY9BEj7n6FyWOvIJKWxtOvzmTFqnW1PqdCx31bs/Sb+HzZArz63lz69zqAvCd/TyQSYcj/\nPcvgE3uE+aYzYswkJo+7ikgkUpmvluekpUUYc91v+HrlGp4fcykAH85exO0PT4ktX5zXX2N49f35\n9D+8E3njhwbLu/UFBg/oSnZmMya8MosR905m8v1DgnU4+WNWrKq778fir35gytjLKN64hb/PXszU\njxbEnu/vX9C/ZwfyHg4yDLnjJQYffyjZmRlMeO2fjHjgTSbfc0GQ743ZrKinb8rIB6cwbsQgmjVN\nZ8GyVUzKmx97vjhu4+Gj/8rd1/2GLSWlfL+6gKtufyH2fHHaRwBG3v0y424+m2ZNm7Dgy5VMeif2\nTo+vvv85/Xt2JO/RK4N1cfuLDD6hS/D+e/VjRtz3OpPvvZhIWoSnJ3/CilUF3HXNKeySm8n1Fx3L\n9RcdC8B/XTOBK0e/xNO3/5aSkjI2l5Ry5eiXYs+Xl0//w428CVcH+UZNDLZvZjMmvDyDEXe/zOQH\nrwjyhetvRzuy37HM/ngGQy85h/LyckbcdBvvTH2D4g0bOGXQGZwy6AyGDzmPJk2a0mbvtpx48mmU\nbNnCn26/ieFDzmdLyRYuuWI4mZlZnH3exdx52x9p2qQpGc2b84cbR+3w11ObVPuVQaQxXpCZNQMu\nBQ4C5gATgJ7AInfflm/e8sxuw+OeK16KP72fzK5DEx2jTsWfPUjSr7/Drk10jDoVf3wXAJl9bkxw\nktoVT78DIPm3cbLvI71GJDpGnYpn/onM7v+T6Bh1Kp59HwAr1m5OcJLatdmlGUCjlxEyT3+80VsE\nxZMu3mHlkEapELj7ZoJ+BNFmNsayREREEiLFKgQ75XUIREREJL4Sf6knERGRnVF52U8/ZieiBoGI\niEhD6JSBiIiIpBpVCERERBoixU4ZqEIgIiIiqhCIiIg0iPoQiIiISKpRhUBERKQh1IdAREREUo0q\nBCIiIg2hCoGIiIikGlUIREREGkK/MhAREZFUowqBiIhIQ6RYH4JIeXKWPJIylIiI7DQijb2AzJPu\nafTvquI3r2n011EhWSsEO2wFiIiINEhyHlA3mPoQiIiISNJWCERERJJbivUhUIVAREREVCEQERFp\nEPUhEBERkVSjCoGIiEhDpFgfgpRuEJhZGjAO6AxsAi5x98WJTVWTmR0O/Mnd+yU6SzQzawpMAPYD\nMoDb3f21hIaKYmbpwGOAEVy74nJ3n5/YVDWZWWtgNnC8uy9IdJ7qzOxToCC8udTdL0xknurM7Hrg\nVKAZMM7dH09wpK3M7ALggvBmc6ALsKe7r01UpmjhPvwUwT5cClyaTO9BM8sAngD2J3gPXuXuixKb\najvolMFO5TSgubsfAYwExiQ4Tw1mdh0wnuDDJNmcA6x29yOBE4EHE5ynulMA3L0P8EfgjsTGqSn8\nQH4EKE50ltqYWXMg4u79wr9kawz0A3oDfYCjgbYJDVSNuz9Zse4IGn3Dk6UxEBoINHH33sCtJN8+\ncimw3t17AcNIvs+Yn5VUbxD0Bd4CcPeZQI/ExqnVEuD0RIeow4vATeH/I0BJArPU4O6vAEPCm/sC\nyfRBXOEu4GFgRaKD1KEzkGVm08zsPTPrlehA1QwA5gEvA5OB1xMbp3Zm1gM42N0fTXSWahYCTcJq\naQtgS4LzVHcQ8CaAuztwYGLjbKfyssb/24FSvUHQAlgXdbvUzJLqNIm7v0Ty7aQAuPt6dy80s1zg\nbwRH4UnF3UvM7CngAeC5ROeJFpaTV7n71ERnqccGgkbLAOBy4Lkk20d+QdCQP4PKfMl4JdMbgFGJ\nDlGL9QSnCxYQnF67P6FpapoDnGxmkbAxuld4KlASINUbBAVAbtTtNHdPqqPcZGdmbYE84Bl3n5jo\nPLVx9/OBTsBjZpad6DxRLgKON7P3Cc4tP21meyY2Ug0LgWfdvdzdFwKrgV8mOFO01cBUd98cHkFu\nBHZPcKYqzGwXwNw9L9FZanENwfrrRFANeio8TZQsJhB8Tn8IDAJmu3tpYiNth/Lyxv/bgVK9QTCd\n4BwaYetzXmLj7FzMbA9gGjDC3SckOk91ZnZu2OEMgiPdsvAvKbj7Ue5+dHh+eQ5wnruvTHCs6i4i\n7FtjZm0IqmrfJTRRVf8ATgyPINsA2QSNhGRyFPBuokPUYQ2VVdIfgaZAMh2B9wTedfe+BKcov0xw\nnp+1ZCoNNoaXCY7QPiI4B55UHaZ2AjcArYCbzKyiL8FJ7p4sHeQmAU+Y2QcEH3RXJ1G2ncXjwJNm\n9g+CX2pclExVNHd/3cyOAj4mOIC5KgmPII3k/SK7B5hgZh8S/ErjBncvSnCmaIuA28zsRoI+QBcn\nOM/2SbGfHSbr8MciIiJJLfOY2xp/+OO8m372wx+LiIgkt7LUOqBO9T4EIiIisg1UIRAREWmIFOtD\noAqBiIiIqEIgIiLSIClWIVCDQOQnmNl+BBfw+YLgp3nNCC5FfKG7f9PAeV4A9HP3C8xsCsHAW7Ve\n3tjMRgHvuPuH2zH/cnePVLvvFgB3v6We5y0Lcy3bxuX85DxFZOegBoHItlnh7l0qbpjZaILLJQ+K\ndcbuPvAnHnI0wdUiRSSZpNjP9tUgEGmYDwiG5K04qp5FcHniipEhryboozOb4GI6G83sXILxIAqA\n5QTXmd96VA6sBMYSDMq1BbiNYNjpHsB4MxtEMGriQ8BuBFdnHObun4VVjGeBHGDmT4U3s6HAuQRX\n/isDBrv7v8LJt5hZZ4LLBF/m7vnhVSsfIRhtsAy43t3f2a41JiJJTZ0KRbZTOKTxYIJLY1d4092N\n4Dr7lwK9w4rCv4Frw8vu/j/BZW6PoOoYGxWGEXyhHwgcB9wMPA98QnBKYR7B2PbXuXs3gpEenw+f\n+yDwZLjM6dVnXC1/C4Khwfu5+yHAK8CVUQ9Z5O5dCRokT4X33QdMcPfuBA2hR8JBr0R+vlJstENV\nCES2TRszmxP+P4PgUrojo6bPCv89BugIzDQzCPobfAr0Bj5y9+8BzOxZ4NhqyzgaeNTdywiqBQeH\njyX8N4fg2u9PVNwH5JjZbgQVhrPD+54juCRxrdy9wMx+C5xlZp0IKhpzoh4yPnzcFDN7Nhy85zjg\nADO7NXxMU6B9XcsQkZ2PGgQi26ZKH4JaVIyhkA781d2Hw9Yv8SYEX/7RFbnaxguoMgy2mXUAvoq6\nKx3YWK0vw94Eg9aUR82/nHoGeQpHsHyfoKrwJkHjo2s92TaHy+7v7j+G82gDfE9QaRD5eUqxPgQ6\nZSASX+8Dg8ystZlFCM73X00wal8vM9vLzNIITjlU9wFwZjiyX2vg7wTViBKgibuvAxaZ2TkAZnZ8\n+ByAd4Bzwv+fHj6vLj2Bxe5+D0Fl4ySqjoD3u3D+g4AF7r4BeI/wtIKZHQTkA1nbtkpEZGegBoFI\nHLn7XGAUwRfo5wT72J3hqYJhBF/cHxN0LKxuHFAEzA0fN8zdC4G3gIfNrDfBl/UlZpYPjCboDFgO\nDAX+O7x/IFBYT8xpQJqZfUHQAXEZ0C5qeqfw9MjvgfPD+4YRNGjygReAc8NsIj9fKdaHQKMdioiI\nNEDmESMbf7TDGXdqtEMREZGklmIH1GoQiIiINESKXbpYfQhEREREFQIREZEGSbFTBqoQiIiIiCoE\nIiIiDaI+BCIiIpJqVCEQERFpCPUhEBERkVSjCoGIiEhDqA+BiIiIpBpVCERERBoiwX0IwpFTxwGd\ngU3AJe6+OGr6KcDNBCOmTnD3x+qbnyoEIiIiO6fTgObufgQwEhhTMcHMmgL3ACcARwNDzGyP+mam\nBoGIiEhDJH74474Ew6Pj7jOBHlHTDgQWu/sad98M/AM4qr6Z6ZSBiIhIAxR/9uAOG5q4Di2AdVG3\nS82sibuX1DKtEGhZ38xUIRAREdk5FQC5UbfTwsZAbdNygbX1zUwNAhERkZ3TdGAggJn1AuZFTfsX\n0NHMdjWzZgSnC2bUN7NIeYpdaUlEROTnIOpXBocCEeBCoBuQ4+6PRv3KII3gVwZj65ufGgQiIiKi\nUwYiIiKiBoGIiIigBoGIiIigBoGIiIigBoGIiIigBoGIiIigBoGIiIigBoGIiIgA/wGrPsPJJqc1\nygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ec77c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "sns.heatmap(cm_normalized, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n",
    "plt.ylabel('Actual label');\n",
    "plt.xlabel('Predicted label');\n",
    "all_sample_title = 'Accuracy Score: {:.3f}'.format(score) \n",
    "plt.title(all_sample_title, size = 15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display Misclassified images with Predicted Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "index = 0\n",
    "misclassifiedIndexes = []\n",
    "for label, predict in zip(test_lbl, predictions):\n",
    "    if label != predict: \n",
    "        misclassifiedIndexes.append(index)\n",
    "    index +=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e63ea10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,4))\n",
    "for plotIndex, badIndex in enumerate(misclassifiedIndexes[0:5]):\n",
    "    plt.subplot(1, 5, plotIndex + 1)\n",
    "    plt.imshow(np.reshape(test_img[badIndex], (28,28)), cmap=plt.cm.gray)\n",
    "    plt.title('Predicted: {}, Actual: {}'.format(predictions[badIndex], test_lbl[badIndex]), fontsize = 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking Performance Based on Training Set Size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A confusion matrix is a table that is often used to describe the performance of a classification model (or \"classifier\") on a set of test data for which the true values are known. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regr = LogisticRegression(solver = 'lbfgs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xWh3UqLD7JXHtrapzY3ooU0qhdPLpt/KdmTkZc+rWwSKbJfx/Nmacz3kGoYhc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e63e3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize = (24,8));\n",
    "plt.tight_layout()\n",
    "\n",
    "for plotIndex, sample_size in enumerate([100, 1000, 60000]):\n",
    "    X_train = train_img[:sample_size].reshape(sample_size, 784)\n",
    "    y_train = train_lbl[:sample_size]\n",
    "    regr.fit(X_train, y_train)\n",
    "    predicted = regr.predict(test_img)\n",
    "    cm = metrics.confusion_matrix(test_lbl, predicted)\n",
    "    cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "    sns.heatmap(cm_normalized, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r', ax = axes[plotIndex], cbar = False);\n",
    "    accuracyString = '{:g} Training Samples Score: {:.3f}'.format(sample_size, regr.score(test_img, test_lbl)) \n",
    "    axes[plotIndex].set_title(accuracyString, size = 25);\n",
    "\n",
    "axes[0].set_ylabel('Actual label', fontsize = 30);\n",
    "axes[1].set_xlabel('Predicted label', fontsize = 30);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<b>if this tutorial doesn't cover what you are looking for, please leave a comment on the youtube video and I will try to cover what you are interested in. </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[youtube video](https://www.youtube.com/watch?v=71iXeuKFcQM)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
